# Online Course Model of Social and Political Education Using Deep Learning

Authors: Min Zhang; Qiong Gao
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

This study aims to improve the social and political literacy of college students. Social and Political Education (SPE) is studied for undergraduates. Firstly, the background of the subject research is introduced. The face recognition module is built based on deep convolutional neural network (DCNN). The sociopolitical situation of the study subjects is analyzed through a questionnaire. Secondly, a model of the learning process is constructed. Finally, the SPE online course learning platform is constructed. Empirical studies are divided into experimental and control groups. The findings show that all model assumptions are valid. There is a significant structural relationship between the influencing factors of the SPE learning process of college students in the study area. The students selected as research objects lack innovation and critical thinking in the learning process and have certain deficiencies in innovative thinking and critical thinking. The questionnaire has good reliability and validity. The predicted data of the designed platform are compared with the predicted data of the control group. Social science competencies by gender are compared. The results showed little difference in the effectiveness of students using other methods for sociopolitical learning. The data of the experimental group before and after the test are quite different, indicating that the designed experimental platform has played a certain positive role. There are significant differences in the posttest data between the experimental group and the control group, indicating that the constructed online course learning model has a positive impact on students’ innovative thinking and critical thinking. Women’s learning motivation and transfer learning ability are stronger than those of men. The constructed model has certain feasibility for the learning of SPE online courses with face recognition module. These contents provide a reference for the reform of social and political courses.


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## Body

## 1. Introduction

### 1.1. Conceptual or Theoretical Framework

As being the main force in building the country, college students are urged to improve their social and political quality. Through the development of social and political courses, colleges and universities shape the world outlook, outlook on life, and values of college students and promote the healthy growth of college students [1]. The establishment of social and political courses in colleges and universities is the main means for colleges and universities to carry out Social and Political Education (SPE) to college students. Therefore, the development and reform of political thought courses in colleges and universities have been widely concerned [2]. Traditional social and political courses are mostly led by teachers, who conduct conceptual social and political knowledge comments. Under this kind of teaching method, students’ enthusiasm for participation is not high. Meanwhile, the effectiveness of social and political classrooms in improving students’ social and political literacy is not high [3].Deep learning (DL) technology is widely used in the calculation of big data resources. If big data is the raw material, then DL is the raw material processing plant. DL technology is highly intelligent. In recent years, it has been applied in the field of SPE. DL algorithms can capture, filter, model, and analyze the big data of human consciousness in the process of SPE. With the continuous use and analysis of SPE big data resources by DL algorithms, the SPE resources analyzed by artificial intelligence (AI) will become more and more regular and systematic. Liu pointed out that DL algorithms can more accurately predict the big data of educators’ thoughts and behaviors by analyzing this data. It can determine targeted educational content, adopt customized methods to carry out education, and achieve the precise teaching [4]. Under the influence of DL technology, SPE in the new era has developed better. However, intelligent social politics cannot replace the traditional form of SPE. The combination of intelligent social and political and traditional social and political can complement each other. Intelligent SPE can accurately teach, self-innovate, and expand the scope of education, creating a tailor-made social and political program for each educated person. Zhang et al. used AI-based precision SPE and found that smart SPE can achieve precise identification, precise customization, precise transmission, and precise feedback [5]. Zhang et al. combined DL technology with VR technology and SPE to create a more unique education model [6].Due to the low efficiency of SPE in colleges and universities at this stage, deep learning technology has been introduced to study the learning model of college students’ SPE online courses. The hypothetical model construction based on deep learning technology introduces the influencing factors of deep learning technology and the status quo of college students’ SPE. The designed questionnaire is used to construct the structural equation modeling (SEM). Finally, empirical research is analyzed. Section2 is the analysis of the status quo of SPE research for college students. Section 3 describes the design of SPE online courses in colleges and universities. Section 4 constructs an online platform for SPE in colleges and universities. Section 5 analyzes the construction results of the college SPE online course model. The innovation is that for the first time, deep learning technology is introduced to the study of SPE of college students. The SEM is used to analyze the relationship between the influencing factors of college students’ deep learning. This study focuses on the mechanism of the learning process. The research content is that the improvement of college students’ social and political literacy in the new era has a positive effect.


### 1.2. Related Research

Machine learning has a pivotal position in the field of artificial intelligence (AI). Artificial Neural Network (ANN) is one of the very active branches in the field of computational intelligence and machine learning [7, 8]. In recent years, deep learning algorithms are a breakthrough in the field of machine learning research [9, 10]. At the beginning of the 21st century, Canadian scholars put forward the concept of deep learning and started the research boom of deep learning [11, 12]. After years of development, deep learning technology has achieved outstanding performance in the fields of computer vision, natural language processing, and speech recognition [13]. The application of deep learning technology in the field of education has promoted the transformation of learning management models and teaching models [14]. The deep learning open-source framework has the characteristics of strong flexibility and portability, including Caffe, TensorFlow, Theano, and Keras. Therefore, it is widely used in the field of education [15, 16].The application of deep learning technology is less in the graduation thesis of undergraduates majoring in social sciences. The discussion of DL technology focuses on the discussion of related social science issues and only stays on the method of use, such as the prediction of academic performance [17]. The application research of DL technology in teaching is mainly focused on the application of social science master’s thesis online examination and other education systems [18]. Researchers proposed a DL-based target detection algorithm and designed an online examination abnormal behavior detection system. The system realizes automatic proctoring [19]. The social science doctoral dissertation mainly studies text mining technology based on DL technology. An extraction model that integrates the relationship between sentences is proposed. The model is used to train relational classifiers based on existing natural language syntactic structure information. Finally, this model lays the foundation for the application of distance education in the field of education [20]. The detailed curriculum design plan for undergraduate thesis, master thesis, and doctoral thesis is shown in Figure 1. In the process of searching for documents, there are relatively small differences in the curriculum structure design in the undergraduate thesis, master thesis, and doctoral thesis, but there are obvious differences in the use of DL technology, as shown in Figure 1.Figure 1
Summary of DL usage in undergraduate, master, and doctoral dissertations.There are generally little researches on deep learning technology in the field of social sciences, and there are few practical applications in the field of education, and most of them stay on the theoretical level. Therefore, university SPE is researched based on the main points of deep learning technology research. A practical SPE network course learning system for college students has been constructed.


### 1.3. Purpose of the Study

The realization path of learner dynamic learning is deeply researched in the research area. Related literature is analyzed and sorted out. This summarizes the important factors that affect learners’ dynamic learning and finds out the structural and quantitative relationships between the dimensions of the dynamic learning process. A hypothetical model based on deep learning is constructed according to the internal connections of each dimension. This provides theoretical support for college students to improve their social and political literacy. On the other hand, the study of college students is analyzed for differences. Students in the study area have been designated online learning courses, and online course learning models have been proposed. The innovations are not directly explained in computational biology. Through questionnaires, scales, and evaluation systems, structural equation models and modified research hypothesis models are used to analyze SPE. This is conducive to the development of social and political research. Here, the login link of the social and political system uses CNN. The innovative combination of SPE system and CNN-based facial recognition can improve the efficiency of students’ online social and political examinations. This provides a reference for the development of neural networks in the field of education.The main scientific contributions in the field of mathematics and computational biology are the reliability and validity analysis of structural models and questionnaires are carried out in detail mathematical analysis. The innovation is that the structural model in the field of mathematics, the reliability and validity analysis of the questionnaire, and thet-test are introduced into the construction of the education platform, which belongs to the combination of theory and practice. A significant contribution in the field of neuroscience is the proposal of a novel atrous convolutional network for facial expression recognition in low-resolution images. The innovation is that the atrous convolutional network belongs to a relatively unknown facial recognition algorithm in the field of neuroscience. The research belongs to the combination of theory and practice. The study will be applied in the field of international teaching, and the primary global users will be university students in various countries.


## 1.1. Conceptual or Theoretical Framework

As being the main force in building the country, college students are urged to improve their social and political quality. Through the development of social and political courses, colleges and universities shape the world outlook, outlook on life, and values of college students and promote the healthy growth of college students [1]. The establishment of social and political courses in colleges and universities is the main means for colleges and universities to carry out Social and Political Education (SPE) to college students. Therefore, the development and reform of political thought courses in colleges and universities have been widely concerned [2]. Traditional social and political courses are mostly led by teachers, who conduct conceptual social and political knowledge comments. Under this kind of teaching method, students’ enthusiasm for participation is not high. Meanwhile, the effectiveness of social and political classrooms in improving students’ social and political literacy is not high [3].Deep learning (DL) technology is widely used in the calculation of big data resources. If big data is the raw material, then DL is the raw material processing plant. DL technology is highly intelligent. In recent years, it has been applied in the field of SPE. DL algorithms can capture, filter, model, and analyze the big data of human consciousness in the process of SPE. With the continuous use and analysis of SPE big data resources by DL algorithms, the SPE resources analyzed by artificial intelligence (AI) will become more and more regular and systematic. Liu pointed out that DL algorithms can more accurately predict the big data of educators’ thoughts and behaviors by analyzing this data. It can determine targeted educational content, adopt customized methods to carry out education, and achieve the precise teaching [4]. Under the influence of DL technology, SPE in the new era has developed better. However, intelligent social politics cannot replace the traditional form of SPE. The combination of intelligent social and political and traditional social and political can complement each other. Intelligent SPE can accurately teach, self-innovate, and expand the scope of education, creating a tailor-made social and political program for each educated person. Zhang et al. used AI-based precision SPE and found that smart SPE can achieve precise identification, precise customization, precise transmission, and precise feedback [5]. Zhang et al. combined DL technology with VR technology and SPE to create a more unique education model [6].Due to the low efficiency of SPE in colleges and universities at this stage, deep learning technology has been introduced to study the learning model of college students’ SPE online courses. The hypothetical model construction based on deep learning technology introduces the influencing factors of deep learning technology and the status quo of college students’ SPE. The designed questionnaire is used to construct the structural equation modeling (SEM). Finally, empirical research is analyzed. Section2 is the analysis of the status quo of SPE research for college students. Section 3 describes the design of SPE online courses in colleges and universities. Section 4 constructs an online platform for SPE in colleges and universities. Section 5 analyzes the construction results of the college SPE online course model. The innovation is that for the first time, deep learning technology is introduced to the study of SPE of college students. The SEM is used to analyze the relationship between the influencing factors of college students’ deep learning. This study focuses on the mechanism of the learning process. The research content is that the improvement of college students’ social and political literacy in the new era has a positive effect.


## 1.2. Related Research

Machine learning has a pivotal position in the field of artificial intelligence (AI). Artificial Neural Network (ANN) is one of the very active branches in the field of computational intelligence and machine learning [7, 8]. In recent years, deep learning algorithms are a breakthrough in the field of machine learning research [9, 10]. At the beginning of the 21st century, Canadian scholars put forward the concept of deep learning and started the research boom of deep learning [11, 12]. After years of development, deep learning technology has achieved outstanding performance in the fields of computer vision, natural language processing, and speech recognition [13]. The application of deep learning technology in the field of education has promoted the transformation of learning management models and teaching models [14]. The deep learning open-source framework has the characteristics of strong flexibility and portability, including Caffe, TensorFlow, Theano, and Keras. Therefore, it is widely used in the field of education [15, 16].The application of deep learning technology is less in the graduation thesis of undergraduates majoring in social sciences. The discussion of DL technology focuses on the discussion of related social science issues and only stays on the method of use, such as the prediction of academic performance [17]. The application research of DL technology in teaching is mainly focused on the application of social science master’s thesis online examination and other education systems [18]. Researchers proposed a DL-based target detection algorithm and designed an online examination abnormal behavior detection system. The system realizes automatic proctoring [19]. The social science doctoral dissertation mainly studies text mining technology based on DL technology. An extraction model that integrates the relationship between sentences is proposed. The model is used to train relational classifiers based on existing natural language syntactic structure information. Finally, this model lays the foundation for the application of distance education in the field of education [20]. The detailed curriculum design plan for undergraduate thesis, master thesis, and doctoral thesis is shown in Figure 1. In the process of searching for documents, there are relatively small differences in the curriculum structure design in the undergraduate thesis, master thesis, and doctoral thesis, but there are obvious differences in the use of DL technology, as shown in Figure 1.Figure 1
Summary of DL usage in undergraduate, master, and doctoral dissertations.There are generally little researches on deep learning technology in the field of social sciences, and there are few practical applications in the field of education, and most of them stay on the theoretical level. Therefore, university SPE is researched based on the main points of deep learning technology research. A practical SPE network course learning system for college students has been constructed.


## 1.3. Purpose of the Study

The realization path of learner dynamic learning is deeply researched in the research area. Related literature is analyzed and sorted out. This summarizes the important factors that affect learners’ dynamic learning and finds out the structural and quantitative relationships between the dimensions of the dynamic learning process. A hypothetical model based on deep learning is constructed according to the internal connections of each dimension. This provides theoretical support for college students to improve their social and political literacy. On the other hand, the study of college students is analyzed for differences. Students in the study area have been designated online learning courses, and online course learning models have been proposed. The innovations are not directly explained in computational biology. Through questionnaires, scales, and evaluation systems, structural equation models and modified research hypothesis models are used to analyze SPE. This is conducive to the development of social and political research. Here, the login link of the social and political system uses CNN. The innovative combination of SPE system and CNN-based facial recognition can improve the efficiency of students’ online social and political examinations. This provides a reference for the development of neural networks in the field of education.The main scientific contributions in the field of mathematics and computational biology are the reliability and validity analysis of structural models and questionnaires are carried out in detail mathematical analysis. The innovation is that the structural model in the field of mathematics, the reliability and validity analysis of the questionnaire, and thet-test are introduced into the construction of the education platform, which belongs to the combination of theory and practice. A significant contribution in the field of neuroscience is the proposal of a novel atrous convolutional network for facial expression recognition in low-resolution images. The innovation is that the atrous convolutional network belongs to a relatively unknown facial recognition algorithm in the field of neuroscience. The research belongs to the combination of theory and practice. The study will be applied in the field of international teaching, and the primary global users will be university students in various countries.


## 2. Materials and Methods

### 2.1. Theoretical Basis

The constructive learning theory and Bloom’s cognitive goal theory are introduced in Figure2.Figure 2
Theoretical support of the model.In Figure2, the theoretical knowledge provides theoretical support for some critical thinking and innovative thinking training models. The online training model of higher-order thinking by the theory of constructivism and Bloom’s cognitive goal theory will be implemented in in-depth learning.The face recognition algorithm is based on deep convolutional networks and is constructed due to the excellent image recognition capabilities of Convolutional Neural Networks (CNN). Firstly, the data is preprocessed. Secondly, the linear decoder is used to extract the local statistical features of the image as the convolution kernel of CNN. The extracted convolution kernel is used to convolve the image, extract the convolutional features of the image, and pool it. Finally, the SoftMax classifier is used to classify the extracted convolutional features to complete face recognition. The proposed algorithm needs to train the linear decoder and the SoftMax classifier separately. The steps of network training include the following: (1) after the training set images are randomly sampled and preprocessed, the sample block set is obtained. (2) The collection of sample blocks is used as the input of the linear decoder to train the linear decoder. (3) The trained linear decoder is used as a convolution kernel to extract the convolution features of the training set. (4) The average pooling strategy is used to pool the convolved feature maps. (5) The pooled convolutional features are concatenated together. After they are normalized, the image features are used as the input of the SoftMax classifier to train the SoftMax classifier. The test set image is recognized, after training the grid. The steps include the following: (1) the trained linear decoder is used to extract the convolutional features of the test machine; (2) the average pooling strategy is used to pool the feature maps obtained after convolution; and (3) the pooled convolutional features are concatenated. They are normalized and used as the input of the SoftMax classifier for image recognition. The training of the linear decoder is divided into training and fine-tuning. Finally, the optimized network parameters are obtained. The excitation function is introduced in the input layer of the linear decoder. Average pooling is used to pool the convolutional features for consideration of the amount of calculation.Since critical thinking and innovative thinking are important components of higher-order thinking, they are especially important in the cultivation of students. Therefore, students from a certain university are regarded as the research object. Based on the constructivism theory, Bloom’s cognitive goal theory, and thinking-based teaching mode, the model is constructed for students’ online critical thinking and innovative thinking training [21, 22].Face recognition images are collected before the start of the formal experiment.By the previous content, the influencing factors of the DL process of college students’ SPE are identified as comprehension and understanding ability, memorization ability, learning ability, cooperation ability, learning transferability, evaluation and reflection ability, problem-solving ability, innovative thinking, and critical thinking. A hypothetical model of influencing factors of SPE is constructed, and the specific model is shown in Figure3 [23, 24].Figure 3
Hypothetical model of influencing factors of SPE.Figure3 is a theoretical model of the hypothesis of influencing factors of SPE. Among them, the degree of learning develops from shallow to deep, and the learning stages involved include prelearning, memory, comprehension, English and analysis, evaluation, and creation. Each learning stage has specific ability requirements. J1–J15 marked in Figure 1 are the proposed influencing factor hypotheses. Hypothesis 1, that is, J1, shows the relationship between learning motivation and learners' memory ability J2 shows the relationship between memorization ability and comprehension and understanding ability. The specific relationship of other hypotheses is also given in Figure 2. Combined with the content of Figure 3, the hypothesis is summarized and introduced, as shown in Figure 4.Figure 4
The hypothesis of influencing factors.Figure4 shows that Hypothesis 1 is constructed using the idea of learning motivation, Hypothesis 2 is constructed using shallow learning, and Hypotheses 3–15 are constructed using deep understanding. Learning motivation is the internal driving force that directly promotes a person’s behavioral activities. Shallow learning includes the ability to remember, comprehend, and understand. DL requires learners to remember and understand deeply. Therefore, all assumptions here are significant positive-influence relationships. Later, by the 15 assumptions, a structural model of the influencing factors will be built.


### 2.2. Research Process

#### 2.2.1. Research Model

According to the content of the previous study, several SPE curriculum design cases and student learning background theories have been referred to develop college SPE online curriculum design [25]. The main content and goals of the social and political courses in colleges and universities are shown in Figure 5.Figure 5
Main content and goals of SPE in colleges and universities.In Figure5, SPE is carried out based on today’s college SPE courses. The basic theoretical courses used are consistent with the current social and political courses in colleges and universities, mainly including political science, history, ethnology, law, and art. Here, the DL concept is used to learn SPE.The online course design adopts the Attention, Relevance, Confidence, Satisfaction (ARCS) model. The model is composed of relevance, attention, satisfaction, and self-confidence [26]. These four aspects have laid a certain foundation for students’ learning activities. The online course design is carried out from the course resources, learning tasks, learning goals, and learning evaluation of SPE. The specific model diagram is shown in Figure 6.Figure 6
Model diagram of online course design.Figure6 shows that learning resources affect students’ attention, learning tasks affect students’ learning, evaluation feedback affects students’ self-confidence, and learning goals directly affect students’ learning satisfaction. The content of resource design, learning task design, evaluation feedback design, and learning objective design are introduced in the above model framework, as shown in Figure 7.Figure 7
The specific content of curriculum design.Figure7 shows that the online course design is carried out since personalized learning. The whole includes three parts: recommendation, diagnosis, and evaluation [27, 28]. The curriculum design follows students’ development law and cultural background in different growth environments and ultimately improves students’ cognitive level.This paper constructs an online learning experimental platform for students to learn SPE. The functions of online learning experiment platform include login module and learning module. The specific design framework is shown in Figure8.Figure 8
Design framework of online learning experiment platform.In Figure8, the learning module includes personalized recommendation, learning forum, diagnosis, course, and personal center modules [29].The contents of the two modules are introduced. The first is the login module, and the specific process is shown in Figure9.Figure 9
Flowchart of the login module.In Figure9, the login module has three functions: registration, password retrieval, and log in. When students register and log in on the platform, the system will automatically jump to the corresponding ethnic interface according to ethnic attributes [30].The student’s learning module includes five modules: diagnosis module, personalized recommendation module, course learning module, learning forum module, and personal center module. The specific content is shown in Figure10.Figure 10
The student learning module design. (a) The diagnosis module, (b) the personalized recommendation module, (c) the course learning module, and (d) the learning forum module.
(a)(b)(c)(d)In Figure10, the purpose of the four modules is to improve students’ learning enthusiasm in the design of the student forum. The main content of the personal center module is to cultivate students’ innovation and critical thinking. This module generates student evaluation reports.The entity attributes of students, cognitive diagnosis results, serious diagnosis questionnaire, and questionnaire answers are shown in Figure11.Figure 11
The attributes of the diagnosis part of the entity. (a–d) The entity attributes of the student, the cognitive diagnosis result, the serious diagnosis questionnaire, and the questionnaire answer, respectively.The entity attributes of the curriculum resource library, Minority sensitivity, recommended resource library, and published posts are shown in Figure12.Figure 12
Some entity attributes of the resource library. (a–d) The course resource library, minority sensitivity, recommended resource library, and published posts.The entity attributes of a forum like reply posts, works, works likes, and learning behavior are shown in Figure13.Figure 13
Entity attributes of posting part. (a–e) The entity attributes of forum likes, reply posts, works, works likes, and learning behaviors.Figures11–13 are the entity attribute content of the designed platform. Among them, idx is the primary key [31]. On the connection between the entities in the above figure, the specific relationship of the online learning platform is obtained, and the performance of the online learning platform is evaluated.


#### 2.2.2. Participants

The design is divided into two parts. Participants in the questionnaire survey on college students’ SPE status quo are 240 college students from a university in the research area. Thirty students participate in social and political learning under different education models.


#### 2.2.3. Data Collection Tools

The reliability and validity of the questionnaire are tested using Statistical Product and Service Solutions (SPSS) 22.0. Analysis of the questionnaire is analyzed using Analysis of Moment Structure (AMOS) 21.0 and SPSS 22.0 software. After checking the fit of the SEM, AMOS 21.0 software is used to import and analyze the 210 collected data. The path analysis results and fitting results of SPE for college students are obtained. The online course design here is carried out using the Attention, Relevance, Confidence, Satisfaction (ARCS) model.The development environment of the learning platform is shown in Table1.Table 1
Development environment of the platform.
AttributesContentDatabaseMySQLDevelopment toolsIntelliJ IDEA, Adobe Dreamweaver CS5Development languageCSS, JSP, JAVA, and JavaScriptOperating systemWindows 7Table1 shows that the structured query language (MySQL) is adopted as the database management software. In Table 1, the final Entity Relationship Diagram (ERD) is obtained by analyzing the relationship between entities in the online learning platform [32]. The entities included in this learning platform system are cognitive diagnosis results, cognitive diagnosis answers, students, cognitive diagnosis questionnaires, personalized recommendation resource libraries, curriculum learning resource libraries, forums, minority sensitive words, posts, works, forum replies, and learning behavior.


#### 2.2.4. Data Collection Process

The online method is used to assess the social and political system. Therefore, the student’s location and region are not restricted. At this time, facial recognition is given higher requirements. VGGNet is chosen as the basis of the research. Among them, due to the expansibility of VGG16 and the strong ability of image feature extraction, experiments are carried out based on it.Dilated Convolution can significantly improve the accuracy of good models. Therefore, the concept of Dilated Convolution is introduced, as shown in(1)ym,n=∑i=1M∑j=1Nxm+r×i,n+r×jwi,j,xm,n indicates input. wi,j represents a filter with length M and width N. ym,n is the corresponding output after the input passes through the filter. The parameter R represents the void rate. If r = 1, divided convolution is an ordinary convolution calculation process.In CNN structure, the reduction of image size is mainly completed in the pool layer. On the premise of preserving the spatial information of the image as much as possible, the pooling layer increases the receptive field of the convolution kernel, extracts higher-level features, reduces the amount of network parameters, and prevents overfitting. However, increasing the receptive field is a destructive way of the pool layer. This will lead to the loss of internal data structure and spatial hierarchical information. In order to reduce pooling operations, divided revolution is used to reconstruct CNN. Combined with the benchmark network comparison experiment results, VGG-16 is selected as the front end of the model. The divided revolution is deployed as the back end. In this way, while maintaining the resolution, the receptive field increases and deeper significance information and features are extracted. Through this innovative structure, the performance has been effectively improved based on the original model. “1-2-3” is the optimal combination mode by comparing different combination modes. Hyperparameter setting is the process of learning rate setting: the front-end network learning rate is set to 0 for several iterations. After that, a smaller learning rate is used to train the entire network model. In the structure of the back end of the frame, the learning rate is set to 0.001 and the attenuation coefficient is set to 0.0005.In the aspect of data preprocessing, individual dataset images are subjected to face detection and alignment. The image information containing only the face part is saved. The color information of image is easily affected by illumination, and RGB changes greatly. Therefore, before the image is input into the model, it is converted into a gray image. Firstly, the original RGB image is converted to the LAB image color domain. Then, the image component is taken out in theL domain; that is, the gray image corresponding to the original image is obtained. In terms of data enhancement, horizontal flip and random rotation are used to expand the number of samples. The rotation angle is set to −45, −30, −15, 15, 30, and 45. The data set is expanded 7 times combined with horizontal flip.There is a certain imbalance between different categories of each data set. The evaluation index of the model is fusion matrix accuracy, as shown in(2)confusion matrix accuracy=12aa+b+dc+d,where a indicates that the real value and the predicted value are correct. b indicates that the true value is correct, and the predicted value is wrong. c indicates that the real value is wrong, and the predicted value is correct. d indicates that both the real value and the predicted value are wrong.This study uses the questionnaire method to investigate the status quo of DL in SPE of college students in the study area. The specific questionnaire question settings are shown in Figures14 and 15.Figure 14
Basic information topic settings of the questionnaire.Figure 15
Influencing factor item setting.In Figures14 and 15, the basic information survey items in the questionnaire include the student’s gender, age, professional direction, school level, and ethnicity. The second part of the questionnaire is by the nine DL capabilities. It includes learning motivation, memorization ability, comprehension and understanding ability, transfer learning ability, problem-solving ability, cooperation ability, reflection and evaluation ability, critical thinking, and innovative thinking. It contains 39 topics. The specific topics are developed around the nine DL abilities of SPE. The questionnaire options are set in the form of a Likert scale. The options are divided into “completely nonconforming,” “comparatively conforming,” “general,” “comparatively nonconforming,” and “nonconforming,” and 1–5 are used to indicate [33]. The questionnaire is distributed online through WJX.cn, and the target is university students. A total of 240 questionnaires are distributed and 210 are retrieved. The recovery rate is 87.5%. All returned questionnaires are valid. The basic information results of this questionnaire will be given later. The criterion for judging that the questionnaire is unqualified is that the items are not completed or the answers are consistent. The questionnaire survey is entirely motivated by volunteers’ voluntary participation and the protection of volunteers’ privacy.The reliability and validity of the questionnaire are tested using Statistical Product and Service Solutions (SPSS) 22.0 software, and the calculations are as follows.(3)KMO=CCDD+CC.(4)X2=−n−2m+116lnR.(5)df=m−12.(3) is the calculation equation of Kaiser–Meyer–Olkin (KMO). Among them, DD is the sum of squares of the partial correlation coefficients between variables. CC is the sum of squares of correlation coefficients between variables. (4) and (5) are Bartlett’s test equations. n is the number of records. m is the number of factor analysis variables. |R| is the value of the correlation coefficient matrix R determinant.The reliability of the second part of the questionnaire is tested by Cronbach’s alpha, as shown in(6)A=hn−11−∑i=1hsi2sx2.In (6), h is the number of questions in the questionnaire, sx2 is the variance of the total score, and si2 is the variance of the score of the i item.Analysis of Moment Structure (AMOS) 21.0 and SPSS 22.0 software are used in the questionnaire analysis. SEM is performed using simulation tests of hypotheses [34]. SEM is a statistical method to analyze the relationship between variables by the covariance matrix of variables. SEMs can perform indirect measurements of variables that are difficult to measure directly and accurately. The basic process of SEM is as follows: (1) put forward theoretical hypotheses; (2) analyze the effect of independent variables on dependent variables; and (3) modify the model [35].The fitness test indicators of the structural model include 5 indicators: RESEA,χ2/df, AGFI, CFI, and GFI. Among them, RESEA stands for the mean square and square root of the asymptotic residuals, χ2/df stands for the ratio of chi-square degrees of freedom, AGFI and GFI stand for fitting index, and CFI stands for relative fitting index. The specific expression and parameter evaluation criteria of each index are shown in (7)–(12).(7)RMSEA=f0df.Equation (7) is the asymptotic residual mean square and square root, f0 is the difference function value, and df is the degree of freedom. The adaptation of RMSEA <0.05 is good, and the adaptation of RMSEA <0.09 is reasonable.(8)χ2=n−1FS;Z.(9)FS;Z=trSZ−1+lgZ−lgS−P.Equations (8) and (9) are the chi-square degrees of freedom ratio, S is the actual data matrix, and |Z| is the hypothetical model matrix. If χ2/df is between 1 and 5, the model fits well, and if χ2/df >5, the model needs to be revised.(10)AGFI=1−1−GFInn+12df.(11)GFI=1−FS;ZFS;Z0.Equations (10) and (11) are fitting indices, GFI >0.9, 0.8 is the minimum acceptable value, and AGFI >0.9, 0.8 is the minimum acceptable value.(12)CFI=1−MAXχT2−dfT,0MAXχN2−dfN,0.In (12), χT2 is the actual data chi-square value, χN2 is the hypothetical model chi-square value, and df is the corresponding degree of freedom. CFI >0.9, 0.8 is the minimum acceptable value.After checking the fit of SEM, AMOS 21.0 software is used to import and analyze the 210 collected data, and the path analysis results and fitting resultso of SPE of college students are obtained. On this basis, the hypothesis test of the model is carried out. The test standards include the rationality test and parameter significance test. Among them, the rationality parameter >0, the significance test parameter >1.96, P<0.05, P<0.001 is very significant [36]. The results are given later. At this time, the model is revised according to the hypothesis test result, and the fitted value of the revised structural model is tested.


#### 2.2.5. Data Analysis

The Real-World Effective Faces (RAF) data set is selected for the model recognition accuracy experiment. The data set contains 29,672 with highly diversified facial images downloaded from the Internet. Unlike the images obtained under laboratory conditions, the data set images are face images in natural scenes. Here, the sample is provided with seven basic expression tags and a composite emotion tag. The basic expression data includes 15,339 images, training and test sets, with 12,271 and 3,068 images. They are used in training evaluation models. The accuracy comparison models of face recognition include baseline and BaseDCNN models. The BaseDCNN model is improved by the same front and rear-end structure. Its accuracy is compared with that of the BaseDCNN model.Using the questionnaire method, using the critical thinking questionnaire and innovative thinking questionnaire, students’ innovative thinking and critical thinking are tested before and after using the learning platform. Among the two groups of students, one group used the online learning platform designed for SPE, and the other group used other learning methods and compared the results [37, 38]. The questionnaire includes students’ basic information, the critical thinking part, and the innovative thinking part. The questions are designed regarding the International Critical Thinking Tendency Scale [39]. After the screening, 30 valid questionnaires are obtained. Thirty people are divided into two groups, 15 people in each group, and the test was conducted. The time for learning social and political knowledge is from July 10, 2021, to July 16, 2021. The reliability and validity of the questionnaire are tested. The comparison charts of the pretest results between the experimental group and the control group, before and after the control group, before and after the test group, and between the results of the experimental group and the control group before and after the test are drawn in the result part. Among the study subjects, 16 are males and 14 are females, and the gender distribution is relatively even. Therefore, there is finally increased that the influence of different genders on the social and political knowledge learning ability of college students under the influence of deep learning technology. The experiment is divided into two groups according to gender. SPSS software is used to test the various dimensions of students of different genders. The questionnaire data is imported into SPSS based on the results of the reliability and validity analysis of the questionnaire. The independent-sample t is used to test and analyze the differences in the social science ability of students of different genders based on deep learning technology. The variance between the two groups needs to be judged whether they are equal or similar. The judgment condition is sig. >0.05, and the requirement for testing whether the gender difference is significant is P<0.05.


## 2.1. Theoretical Basis

The constructive learning theory and Bloom’s cognitive goal theory are introduced in Figure2.Figure 2
Theoretical support of the model.In Figure2, the theoretical knowledge provides theoretical support for some critical thinking and innovative thinking training models. The online training model of higher-order thinking by the theory of constructivism and Bloom’s cognitive goal theory will be implemented in in-depth learning.The face recognition algorithm is based on deep convolutional networks and is constructed due to the excellent image recognition capabilities of Convolutional Neural Networks (CNN). Firstly, the data is preprocessed. Secondly, the linear decoder is used to extract the local statistical features of the image as the convolution kernel of CNN. The extracted convolution kernel is used to convolve the image, extract the convolutional features of the image, and pool it. Finally, the SoftMax classifier is used to classify the extracted convolutional features to complete face recognition. The proposed algorithm needs to train the linear decoder and the SoftMax classifier separately. The steps of network training include the following: (1) after the training set images are randomly sampled and preprocessed, the sample block set is obtained. (2) The collection of sample blocks is used as the input of the linear decoder to train the linear decoder. (3) The trained linear decoder is used as a convolution kernel to extract the convolution features of the training set. (4) The average pooling strategy is used to pool the convolved feature maps. (5) The pooled convolutional features are concatenated together. After they are normalized, the image features are used as the input of the SoftMax classifier to train the SoftMax classifier. The test set image is recognized, after training the grid. The steps include the following: (1) the trained linear decoder is used to extract the convolutional features of the test machine; (2) the average pooling strategy is used to pool the feature maps obtained after convolution; and (3) the pooled convolutional features are concatenated. They are normalized and used as the input of the SoftMax classifier for image recognition. The training of the linear decoder is divided into training and fine-tuning. Finally, the optimized network parameters are obtained. The excitation function is introduced in the input layer of the linear decoder. Average pooling is used to pool the convolutional features for consideration of the amount of calculation.Since critical thinking and innovative thinking are important components of higher-order thinking, they are especially important in the cultivation of students. Therefore, students from a certain university are regarded as the research object. Based on the constructivism theory, Bloom’s cognitive goal theory, and thinking-based teaching mode, the model is constructed for students’ online critical thinking and innovative thinking training [21, 22].Face recognition images are collected before the start of the formal experiment.By the previous content, the influencing factors of the DL process of college students’ SPE are identified as comprehension and understanding ability, memorization ability, learning ability, cooperation ability, learning transferability, evaluation and reflection ability, problem-solving ability, innovative thinking, and critical thinking. A hypothetical model of influencing factors of SPE is constructed, and the specific model is shown in Figure3 [23, 24].Figure 3
Hypothetical model of influencing factors of SPE.Figure3 is a theoretical model of the hypothesis of influencing factors of SPE. Among them, the degree of learning develops from shallow to deep, and the learning stages involved include prelearning, memory, comprehension, English and analysis, evaluation, and creation. Each learning stage has specific ability requirements. J1–J15 marked in Figure 1 are the proposed influencing factor hypotheses. Hypothesis 1, that is, J1, shows the relationship between learning motivation and learners' memory ability J2 shows the relationship between memorization ability and comprehension and understanding ability. The specific relationship of other hypotheses is also given in Figure 2. Combined with the content of Figure 3, the hypothesis is summarized and introduced, as shown in Figure 4.Figure 4
The hypothesis of influencing factors.Figure4 shows that Hypothesis 1 is constructed using the idea of learning motivation, Hypothesis 2 is constructed using shallow learning, and Hypotheses 3–15 are constructed using deep understanding. Learning motivation is the internal driving force that directly promotes a person’s behavioral activities. Shallow learning includes the ability to remember, comprehend, and understand. DL requires learners to remember and understand deeply. Therefore, all assumptions here are significant positive-influence relationships. Later, by the 15 assumptions, a structural model of the influencing factors will be built.


## 2.2. Research Process

### 2.2.1. Research Model

According to the content of the previous study, several SPE curriculum design cases and student learning background theories have been referred to develop college SPE online curriculum design [25]. The main content and goals of the social and political courses in colleges and universities are shown in Figure 5.Figure 5
Main content and goals of SPE in colleges and universities.In Figure5, SPE is carried out based on today’s college SPE courses. The basic theoretical courses used are consistent with the current social and political courses in colleges and universities, mainly including political science, history, ethnology, law, and art. Here, the DL concept is used to learn SPE.The online course design adopts the Attention, Relevance, Confidence, Satisfaction (ARCS) model. The model is composed of relevance, attention, satisfaction, and self-confidence [26]. These four aspects have laid a certain foundation for students’ learning activities. The online course design is carried out from the course resources, learning tasks, learning goals, and learning evaluation of SPE. The specific model diagram is shown in Figure 6.Figure 6
Model diagram of online course design.Figure6 shows that learning resources affect students’ attention, learning tasks affect students’ learning, evaluation feedback affects students’ self-confidence, and learning goals directly affect students’ learning satisfaction. The content of resource design, learning task design, evaluation feedback design, and learning objective design are introduced in the above model framework, as shown in Figure 7.Figure 7
The specific content of curriculum design.Figure7 shows that the online course design is carried out since personalized learning. The whole includes three parts: recommendation, diagnosis, and evaluation [27, 28]. The curriculum design follows students’ development law and cultural background in different growth environments and ultimately improves students’ cognitive level.This paper constructs an online learning experimental platform for students to learn SPE. The functions of online learning experiment platform include login module and learning module. The specific design framework is shown in Figure8.Figure 8
Design framework of online learning experiment platform.In Figure8, the learning module includes personalized recommendation, learning forum, diagnosis, course, and personal center modules [29].The contents of the two modules are introduced. The first is the login module, and the specific process is shown in Figure9.Figure 9
Flowchart of the login module.In Figure9, the login module has three functions: registration, password retrieval, and log in. When students register and log in on the platform, the system will automatically jump to the corresponding ethnic interface according to ethnic attributes [30].The student’s learning module includes five modules: diagnosis module, personalized recommendation module, course learning module, learning forum module, and personal center module. The specific content is shown in Figure10.Figure 10
The student learning module design. (a) The diagnosis module, (b) the personalized recommendation module, (c) the course learning module, and (d) the learning forum module.
(a)(b)(c)(d)In Figure10, the purpose of the four modules is to improve students’ learning enthusiasm in the design of the student forum. The main content of the personal center module is to cultivate students’ innovation and critical thinking. This module generates student evaluation reports.The entity attributes of students, cognitive diagnosis results, serious diagnosis questionnaire, and questionnaire answers are shown in Figure11.Figure 11
The attributes of the diagnosis part of the entity. (a–d) The entity attributes of the student, the cognitive diagnosis result, the serious diagnosis questionnaire, and the questionnaire answer, respectively.The entity attributes of the curriculum resource library, Minority sensitivity, recommended resource library, and published posts are shown in Figure12.Figure 12
Some entity attributes of the resource library. (a–d) The course resource library, minority sensitivity, recommended resource library, and published posts.The entity attributes of a forum like reply posts, works, works likes, and learning behavior are shown in Figure13.Figure 13
Entity attributes of posting part. (a–e) The entity attributes of forum likes, reply posts, works, works likes, and learning behaviors.Figures11–13 are the entity attribute content of the designed platform. Among them, idx is the primary key [31]. On the connection between the entities in the above figure, the specific relationship of the online learning platform is obtained, and the performance of the online learning platform is evaluated.


### 2.2.2. Participants

The design is divided into two parts. Participants in the questionnaire survey on college students’ SPE status quo are 240 college students from a university in the research area. Thirty students participate in social and political learning under different education models.


### 2.2.3. Data Collection Tools

The reliability and validity of the questionnaire are tested using Statistical Product and Service Solutions (SPSS) 22.0. Analysis of the questionnaire is analyzed using Analysis of Moment Structure (AMOS) 21.0 and SPSS 22.0 software. After checking the fit of the SEM, AMOS 21.0 software is used to import and analyze the 210 collected data. The path analysis results and fitting results of SPE for college students are obtained. The online course design here is carried out using the Attention, Relevance, Confidence, Satisfaction (ARCS) model.The development environment of the learning platform is shown in Table1.Table 1
Development environment of the platform.
AttributesContentDatabaseMySQLDevelopment toolsIntelliJ IDEA, Adobe Dreamweaver CS5Development languageCSS, JSP, JAVA, and JavaScriptOperating systemWindows 7Table1 shows that the structured query language (MySQL) is adopted as the database management software. In Table 1, the final Entity Relationship Diagram (ERD) is obtained by analyzing the relationship between entities in the online learning platform [32]. The entities included in this learning platform system are cognitive diagnosis results, cognitive diagnosis answers, students, cognitive diagnosis questionnaires, personalized recommendation resource libraries, curriculum learning resource libraries, forums, minority sensitive words, posts, works, forum replies, and learning behavior.


### 2.2.4. Data Collection Process

The online method is used to assess the social and political system. Therefore, the student’s location and region are not restricted. At this time, facial recognition is given higher requirements. VGGNet is chosen as the basis of the research. Among them, due to the expansibility of VGG16 and the strong ability of image feature extraction, experiments are carried out based on it.Dilated Convolution can significantly improve the accuracy of good models. Therefore, the concept of Dilated Convolution is introduced, as shown in(1)ym,n=∑i=1M∑j=1Nxm+r×i,n+r×jwi,j,xm,n indicates input. wi,j represents a filter with length M and width N. ym,n is the corresponding output after the input passes through the filter. The parameter R represents the void rate. If r = 1, divided convolution is an ordinary convolution calculation process.In CNN structure, the reduction of image size is mainly completed in the pool layer. On the premise of preserving the spatial information of the image as much as possible, the pooling layer increases the receptive field of the convolution kernel, extracts higher-level features, reduces the amount of network parameters, and prevents overfitting. However, increasing the receptive field is a destructive way of the pool layer. This will lead to the loss of internal data structure and spatial hierarchical information. In order to reduce pooling operations, divided revolution is used to reconstruct CNN. Combined with the benchmark network comparison experiment results, VGG-16 is selected as the front end of the model. The divided revolution is deployed as the back end. In this way, while maintaining the resolution, the receptive field increases and deeper significance information and features are extracted. Through this innovative structure, the performance has been effectively improved based on the original model. “1-2-3” is the optimal combination mode by comparing different combination modes. Hyperparameter setting is the process of learning rate setting: the front-end network learning rate is set to 0 for several iterations. After that, a smaller learning rate is used to train the entire network model. In the structure of the back end of the frame, the learning rate is set to 0.001 and the attenuation coefficient is set to 0.0005.In the aspect of data preprocessing, individual dataset images are subjected to face detection and alignment. The image information containing only the face part is saved. The color information of image is easily affected by illumination, and RGB changes greatly. Therefore, before the image is input into the model, it is converted into a gray image. Firstly, the original RGB image is converted to the LAB image color domain. Then, the image component is taken out in theL domain; that is, the gray image corresponding to the original image is obtained. In terms of data enhancement, horizontal flip and random rotation are used to expand the number of samples. The rotation angle is set to −45, −30, −15, 15, 30, and 45. The data set is expanded 7 times combined with horizontal flip.There is a certain imbalance between different categories of each data set. The evaluation index of the model is fusion matrix accuracy, as shown in(2)confusion matrix accuracy=12aa+b+dc+d,where a indicates that the real value and the predicted value are correct. b indicates that the true value is correct, and the predicted value is wrong. c indicates that the real value is wrong, and the predicted value is correct. d indicates that both the real value and the predicted value are wrong.This study uses the questionnaire method to investigate the status quo of DL in SPE of college students in the study area. The specific questionnaire question settings are shown in Figures14 and 15.Figure 14
Basic information topic settings of the questionnaire.Figure 15
Influencing factor item setting.In Figures14 and 15, the basic information survey items in the questionnaire include the student’s gender, age, professional direction, school level, and ethnicity. The second part of the questionnaire is by the nine DL capabilities. It includes learning motivation, memorization ability, comprehension and understanding ability, transfer learning ability, problem-solving ability, cooperation ability, reflection and evaluation ability, critical thinking, and innovative thinking. It contains 39 topics. The specific topics are developed around the nine DL abilities of SPE. The questionnaire options are set in the form of a Likert scale. The options are divided into “completely nonconforming,” “comparatively conforming,” “general,” “comparatively nonconforming,” and “nonconforming,” and 1–5 are used to indicate [33]. The questionnaire is distributed online through WJX.cn, and the target is university students. A total of 240 questionnaires are distributed and 210 are retrieved. The recovery rate is 87.5%. All returned questionnaires are valid. The basic information results of this questionnaire will be given later. The criterion for judging that the questionnaire is unqualified is that the items are not completed or the answers are consistent. The questionnaire survey is entirely motivated by volunteers’ voluntary participation and the protection of volunteers’ privacy.The reliability and validity of the questionnaire are tested using Statistical Product and Service Solutions (SPSS) 22.0 software, and the calculations are as follows.(3)KMO=CCDD+CC.(4)X2=−n−2m+116lnR.(5)df=m−12.(3) is the calculation equation of Kaiser–Meyer–Olkin (KMO). Among them, DD is the sum of squares of the partial correlation coefficients between variables. CC is the sum of squares of correlation coefficients between variables. (4) and (5) are Bartlett’s test equations. n is the number of records. m is the number of factor analysis variables. |R| is the value of the correlation coefficient matrix R determinant.The reliability of the second part of the questionnaire is tested by Cronbach’s alpha, as shown in(6)A=hn−11−∑i=1hsi2sx2.In (6), h is the number of questions in the questionnaire, sx2 is the variance of the total score, and si2 is the variance of the score of the i item.Analysis of Moment Structure (AMOS) 21.0 and SPSS 22.0 software are used in the questionnaire analysis. SEM is performed using simulation tests of hypotheses [34]. SEM is a statistical method to analyze the relationship between variables by the covariance matrix of variables. SEMs can perform indirect measurements of variables that are difficult to measure directly and accurately. The basic process of SEM is as follows: (1) put forward theoretical hypotheses; (2) analyze the effect of independent variables on dependent variables; and (3) modify the model [35].The fitness test indicators of the structural model include 5 indicators: RESEA,χ2/df, AGFI, CFI, and GFI. Among them, RESEA stands for the mean square and square root of the asymptotic residuals, χ2/df stands for the ratio of chi-square degrees of freedom, AGFI and GFI stand for fitting index, and CFI stands for relative fitting index. The specific expression and parameter evaluation criteria of each index are shown in (7)–(12).(7)RMSEA=f0df.Equation (7) is the asymptotic residual mean square and square root, f0 is the difference function value, and df is the degree of freedom. The adaptation of RMSEA <0.05 is good, and the adaptation of RMSEA <0.09 is reasonable.(8)χ2=n−1FS;Z.(9)FS;Z=trSZ−1+lgZ−lgS−P.Equations (8) and (9) are the chi-square degrees of freedom ratio, S is the actual data matrix, and |Z| is the hypothetical model matrix. If χ2/df is between 1 and 5, the model fits well, and if χ2/df >5, the model needs to be revised.(10)AGFI=1−1−GFInn+12df.(11)GFI=1−FS;ZFS;Z0.Equations (10) and (11) are fitting indices, GFI >0.9, 0.8 is the minimum acceptable value, and AGFI >0.9, 0.8 is the minimum acceptable value.(12)CFI=1−MAXχT2−dfT,0MAXχN2−dfN,0.In (12), χT2 is the actual data chi-square value, χN2 is the hypothetical model chi-square value, and df is the corresponding degree of freedom. CFI >0.9, 0.8 is the minimum acceptable value.After checking the fit of SEM, AMOS 21.0 software is used to import and analyze the 210 collected data, and the path analysis results and fitting resultso of SPE of college students are obtained. On this basis, the hypothesis test of the model is carried out. The test standards include the rationality test and parameter significance test. Among them, the rationality parameter >0, the significance test parameter >1.96, P<0.05, P<0.001 is very significant [36]. The results are given later. At this time, the model is revised according to the hypothesis test result, and the fitted value of the revised structural model is tested.


### 2.2.5. Data Analysis

The Real-World Effective Faces (RAF) data set is selected for the model recognition accuracy experiment. The data set contains 29,672 with highly diversified facial images downloaded from the Internet. Unlike the images obtained under laboratory conditions, the data set images are face images in natural scenes. Here, the sample is provided with seven basic expression tags and a composite emotion tag. The basic expression data includes 15,339 images, training and test sets, with 12,271 and 3,068 images. They are used in training evaluation models. The accuracy comparison models of face recognition include baseline and BaseDCNN models. The BaseDCNN model is improved by the same front and rear-end structure. Its accuracy is compared with that of the BaseDCNN model.Using the questionnaire method, using the critical thinking questionnaire and innovative thinking questionnaire, students’ innovative thinking and critical thinking are tested before and after using the learning platform. Among the two groups of students, one group used the online learning platform designed for SPE, and the other group used other learning methods and compared the results [37, 38]. The questionnaire includes students’ basic information, the critical thinking part, and the innovative thinking part. The questions are designed regarding the International Critical Thinking Tendency Scale [39]. After the screening, 30 valid questionnaires are obtained. Thirty people are divided into two groups, 15 people in each group, and the test was conducted. The time for learning social and political knowledge is from July 10, 2021, to July 16, 2021. The reliability and validity of the questionnaire are tested. The comparison charts of the pretest results between the experimental group and the control group, before and after the control group, before and after the test group, and between the results of the experimental group and the control group before and after the test are drawn in the result part. Among the study subjects, 16 are males and 14 are females, and the gender distribution is relatively even. Therefore, there is finally increased that the influence of different genders on the social and political knowledge learning ability of college students under the influence of deep learning technology. The experiment is divided into two groups according to gender. SPSS software is used to test the various dimensions of students of different genders. The questionnaire data is imported into SPSS based on the results of the reliability and validity analysis of the questionnaire. The independent-sample t is used to test and analyze the differences in the social science ability of students of different genders based on deep learning technology. The variance between the two groups needs to be judged whether they are equal or similar. The judgment condition is sig. >0.05, and the requirement for testing whether the gender difference is significant is P<0.05.


## 2.2.1. Research Model

According to the content of the previous study, several SPE curriculum design cases and student learning background theories have been referred to develop college SPE online curriculum design [25]. The main content and goals of the social and political courses in colleges and universities are shown in Figure 5.Figure 5
Main content and goals of SPE in colleges and universities.In Figure5, SPE is carried out based on today’s college SPE courses. The basic theoretical courses used are consistent with the current social and political courses in colleges and universities, mainly including political science, history, ethnology, law, and art. Here, the DL concept is used to learn SPE.The online course design adopts the Attention, Relevance, Confidence, Satisfaction (ARCS) model. The model is composed of relevance, attention, satisfaction, and self-confidence [26]. These four aspects have laid a certain foundation for students’ learning activities. The online course design is carried out from the course resources, learning tasks, learning goals, and learning evaluation of SPE. The specific model diagram is shown in Figure 6.Figure 6
Model diagram of online course design.Figure6 shows that learning resources affect students’ attention, learning tasks affect students’ learning, evaluation feedback affects students’ self-confidence, and learning goals directly affect students’ learning satisfaction. The content of resource design, learning task design, evaluation feedback design, and learning objective design are introduced in the above model framework, as shown in Figure 7.Figure 7
The specific content of curriculum design.Figure7 shows that the online course design is carried out since personalized learning. The whole includes three parts: recommendation, diagnosis, and evaluation [27, 28]. The curriculum design follows students’ development law and cultural background in different growth environments and ultimately improves students’ cognitive level.This paper constructs an online learning experimental platform for students to learn SPE. The functions of online learning experiment platform include login module and learning module. The specific design framework is shown in Figure8.Figure 8
Design framework of online learning experiment platform.In Figure8, the learning module includes personalized recommendation, learning forum, diagnosis, course, and personal center modules [29].The contents of the two modules are introduced. The first is the login module, and the specific process is shown in Figure9.Figure 9
Flowchart of the login module.In Figure9, the login module has three functions: registration, password retrieval, and log in. When students register and log in on the platform, the system will automatically jump to the corresponding ethnic interface according to ethnic attributes [30].The student’s learning module includes five modules: diagnosis module, personalized recommendation module, course learning module, learning forum module, and personal center module. The specific content is shown in Figure10.Figure 10
The student learning module design. (a) The diagnosis module, (b) the personalized recommendation module, (c) the course learning module, and (d) the learning forum module.
(a)(b)(c)(d)In Figure10, the purpose of the four modules is to improve students’ learning enthusiasm in the design of the student forum. The main content of the personal center module is to cultivate students’ innovation and critical thinking. This module generates student evaluation reports.The entity attributes of students, cognitive diagnosis results, serious diagnosis questionnaire, and questionnaire answers are shown in Figure11.Figure 11
The attributes of the diagnosis part of the entity. (a–d) The entity attributes of the student, the cognitive diagnosis result, the serious diagnosis questionnaire, and the questionnaire answer, respectively.The entity attributes of the curriculum resource library, Minority sensitivity, recommended resource library, and published posts are shown in Figure12.Figure 12
Some entity attributes of the resource library. (a–d) The course resource library, minority sensitivity, recommended resource library, and published posts.The entity attributes of a forum like reply posts, works, works likes, and learning behavior are shown in Figure13.Figure 13
Entity attributes of posting part. (a–e) The entity attributes of forum likes, reply posts, works, works likes, and learning behaviors.Figures11–13 are the entity attribute content of the designed platform. Among them, idx is the primary key [31]. On the connection between the entities in the above figure, the specific relationship of the online learning platform is obtained, and the performance of the online learning platform is evaluated.


## 2.2.2. Participants

The design is divided into two parts. Participants in the questionnaire survey on college students’ SPE status quo are 240 college students from a university in the research area. Thirty students participate in social and political learning under different education models.


## 2.2.3. Data Collection Tools

The reliability and validity of the questionnaire are tested using Statistical Product and Service Solutions (SPSS) 22.0. Analysis of the questionnaire is analyzed using Analysis of Moment Structure (AMOS) 21.0 and SPSS 22.0 software. After checking the fit of the SEM, AMOS 21.0 software is used to import and analyze the 210 collected data. The path analysis results and fitting results of SPE for college students are obtained. The online course design here is carried out using the Attention, Relevance, Confidence, Satisfaction (ARCS) model.The development environment of the learning platform is shown in Table1.Table 1
Development environment of the platform.
AttributesContentDatabaseMySQLDevelopment toolsIntelliJ IDEA, Adobe Dreamweaver CS5Development languageCSS, JSP, JAVA, and JavaScriptOperating systemWindows 7Table1 shows that the structured query language (MySQL) is adopted as the database management software. In Table 1, the final Entity Relationship Diagram (ERD) is obtained by analyzing the relationship between entities in the online learning platform [32]. The entities included in this learning platform system are cognitive diagnosis results, cognitive diagnosis answers, students, cognitive diagnosis questionnaires, personalized recommendation resource libraries, curriculum learning resource libraries, forums, minority sensitive words, posts, works, forum replies, and learning behavior.


## 2.2.4. Data Collection Process

The online method is used to assess the social and political system. Therefore, the student’s location and region are not restricted. At this time, facial recognition is given higher requirements. VGGNet is chosen as the basis of the research. Among them, due to the expansibility of VGG16 and the strong ability of image feature extraction, experiments are carried out based on it.Dilated Convolution can significantly improve the accuracy of good models. Therefore, the concept of Dilated Convolution is introduced, as shown in(1)ym,n=∑i=1M∑j=1Nxm+r×i,n+r×jwi,j,xm,n indicates input. wi,j represents a filter with length M and width N. ym,n is the corresponding output after the input passes through the filter. The parameter R represents the void rate. If r = 1, divided convolution is an ordinary convolution calculation process.In CNN structure, the reduction of image size is mainly completed in the pool layer. On the premise of preserving the spatial information of the image as much as possible, the pooling layer increases the receptive field of the convolution kernel, extracts higher-level features, reduces the amount of network parameters, and prevents overfitting. However, increasing the receptive field is a destructive way of the pool layer. This will lead to the loss of internal data structure and spatial hierarchical information. In order to reduce pooling operations, divided revolution is used to reconstruct CNN. Combined with the benchmark network comparison experiment results, VGG-16 is selected as the front end of the model. The divided revolution is deployed as the back end. In this way, while maintaining the resolution, the receptive field increases and deeper significance information and features are extracted. Through this innovative structure, the performance has been effectively improved based on the original model. “1-2-3” is the optimal combination mode by comparing different combination modes. Hyperparameter setting is the process of learning rate setting: the front-end network learning rate is set to 0 for several iterations. After that, a smaller learning rate is used to train the entire network model. In the structure of the back end of the frame, the learning rate is set to 0.001 and the attenuation coefficient is set to 0.0005.In the aspect of data preprocessing, individual dataset images are subjected to face detection and alignment. The image information containing only the face part is saved. The color information of image is easily affected by illumination, and RGB changes greatly. Therefore, before the image is input into the model, it is converted into a gray image. Firstly, the original RGB image is converted to the LAB image color domain. Then, the image component is taken out in theL domain; that is, the gray image corresponding to the original image is obtained. In terms of data enhancement, horizontal flip and random rotation are used to expand the number of samples. The rotation angle is set to −45, −30, −15, 15, 30, and 45. The data set is expanded 7 times combined with horizontal flip.There is a certain imbalance between different categories of each data set. The evaluation index of the model is fusion matrix accuracy, as shown in(2)confusion matrix accuracy=12aa+b+dc+d,where a indicates that the real value and the predicted value are correct. b indicates that the true value is correct, and the predicted value is wrong. c indicates that the real value is wrong, and the predicted value is correct. d indicates that both the real value and the predicted value are wrong.This study uses the questionnaire method to investigate the status quo of DL in SPE of college students in the study area. The specific questionnaire question settings are shown in Figures14 and 15.Figure 14
Basic information topic settings of the questionnaire.Figure 15
Influencing factor item setting.In Figures14 and 15, the basic information survey items in the questionnaire include the student’s gender, age, professional direction, school level, and ethnicity. The second part of the questionnaire is by the nine DL capabilities. It includes learning motivation, memorization ability, comprehension and understanding ability, transfer learning ability, problem-solving ability, cooperation ability, reflection and evaluation ability, critical thinking, and innovative thinking. It contains 39 topics. The specific topics are developed around the nine DL abilities of SPE. The questionnaire options are set in the form of a Likert scale. The options are divided into “completely nonconforming,” “comparatively conforming,” “general,” “comparatively nonconforming,” and “nonconforming,” and 1–5 are used to indicate [33]. The questionnaire is distributed online through WJX.cn, and the target is university students. A total of 240 questionnaires are distributed and 210 are retrieved. The recovery rate is 87.5%. All returned questionnaires are valid. The basic information results of this questionnaire will be given later. The criterion for judging that the questionnaire is unqualified is that the items are not completed or the answers are consistent. The questionnaire survey is entirely motivated by volunteers’ voluntary participation and the protection of volunteers’ privacy.The reliability and validity of the questionnaire are tested using Statistical Product and Service Solutions (SPSS) 22.0 software, and the calculations are as follows.(3)KMO=CCDD+CC.(4)X2=−n−2m+116lnR.(5)df=m−12.(3) is the calculation equation of Kaiser–Meyer–Olkin (KMO). Among them, DD is the sum of squares of the partial correlation coefficients between variables. CC is the sum of squares of correlation coefficients between variables. (4) and (5) are Bartlett’s test equations. n is the number of records. m is the number of factor analysis variables. |R| is the value of the correlation coefficient matrix R determinant.The reliability of the second part of the questionnaire is tested by Cronbach’s alpha, as shown in(6)A=hn−11−∑i=1hsi2sx2.In (6), h is the number of questions in the questionnaire, sx2 is the variance of the total score, and si2 is the variance of the score of the i item.Analysis of Moment Structure (AMOS) 21.0 and SPSS 22.0 software are used in the questionnaire analysis. SEM is performed using simulation tests of hypotheses [34]. SEM is a statistical method to analyze the relationship between variables by the covariance matrix of variables. SEMs can perform indirect measurements of variables that are difficult to measure directly and accurately. The basic process of SEM is as follows: (1) put forward theoretical hypotheses; (2) analyze the effect of independent variables on dependent variables; and (3) modify the model [35].The fitness test indicators of the structural model include 5 indicators: RESEA,χ2/df, AGFI, CFI, and GFI. Among them, RESEA stands for the mean square and square root of the asymptotic residuals, χ2/df stands for the ratio of chi-square degrees of freedom, AGFI and GFI stand for fitting index, and CFI stands for relative fitting index. The specific expression and parameter evaluation criteria of each index are shown in (7)–(12).(7)RMSEA=f0df.Equation (7) is the asymptotic residual mean square and square root, f0 is the difference function value, and df is the degree of freedom. The adaptation of RMSEA <0.05 is good, and the adaptation of RMSEA <0.09 is reasonable.(8)χ2=n−1FS;Z.(9)FS;Z=trSZ−1+lgZ−lgS−P.Equations (8) and (9) are the chi-square degrees of freedom ratio, S is the actual data matrix, and |Z| is the hypothetical model matrix. If χ2/df is between 1 and 5, the model fits well, and if χ2/df >5, the model needs to be revised.(10)AGFI=1−1−GFInn+12df.(11)GFI=1−FS;ZFS;Z0.Equations (10) and (11) are fitting indices, GFI >0.9, 0.8 is the minimum acceptable value, and AGFI >0.9, 0.8 is the minimum acceptable value.(12)CFI=1−MAXχT2−dfT,0MAXχN2−dfN,0.In (12), χT2 is the actual data chi-square value, χN2 is the hypothetical model chi-square value, and df is the corresponding degree of freedom. CFI >0.9, 0.8 is the minimum acceptable value.After checking the fit of SEM, AMOS 21.0 software is used to import and analyze the 210 collected data, and the path analysis results and fitting resultso of SPE of college students are obtained. On this basis, the hypothesis test of the model is carried out. The test standards include the rationality test and parameter significance test. Among them, the rationality parameter >0, the significance test parameter >1.96, P<0.05, P<0.001 is very significant [36]. The results are given later. At this time, the model is revised according to the hypothesis test result, and the fitted value of the revised structural model is tested.


## 2.2.5. Data Analysis

The Real-World Effective Faces (RAF) data set is selected for the model recognition accuracy experiment. The data set contains 29,672 with highly diversified facial images downloaded from the Internet. Unlike the images obtained under laboratory conditions, the data set images are face images in natural scenes. Here, the sample is provided with seven basic expression tags and a composite emotion tag. The basic expression data includes 15,339 images, training and test sets, with 12,271 and 3,068 images. They are used in training evaluation models. The accuracy comparison models of face recognition include baseline and BaseDCNN models. The BaseDCNN model is improved by the same front and rear-end structure. Its accuracy is compared with that of the BaseDCNN model.Using the questionnaire method, using the critical thinking questionnaire and innovative thinking questionnaire, students’ innovative thinking and critical thinking are tested before and after using the learning platform. Among the two groups of students, one group used the online learning platform designed for SPE, and the other group used other learning methods and compared the results [37, 38]. The questionnaire includes students’ basic information, the critical thinking part, and the innovative thinking part. The questions are designed regarding the International Critical Thinking Tendency Scale [39]. After the screening, 30 valid questionnaires are obtained. Thirty people are divided into two groups, 15 people in each group, and the test was conducted. The time for learning social and political knowledge is from July 10, 2021, to July 16, 2021. The reliability and validity of the questionnaire are tested. The comparison charts of the pretest results between the experimental group and the control group, before and after the control group, before and after the test group, and between the results of the experimental group and the control group before and after the test are drawn in the result part. Among the study subjects, 16 are males and 14 are females, and the gender distribution is relatively even. Therefore, there is finally increased that the influence of different genders on the social and political knowledge learning ability of college students under the influence of deep learning technology. The experiment is divided into two groups according to gender. SPSS software is used to test the various dimensions of students of different genders. The questionnaire data is imported into SPSS based on the results of the reliability and validity analysis of the questionnaire. The independent-sample t is used to test and analyze the differences in the social science ability of students of different genders based on deep learning technology. The variance between the two groups needs to be judged whether they are equal or similar. The judgment condition is sig. >0.05, and the requirement for testing whether the gender difference is significant is P<0.05.


## 3. Result

### 3.1. Face Recognition Accuracy Comparison

The accuracy comparison of face recognition is shown in Figure16.Figure 16
Face recognition accuracy comparison. (a) The comparison between baseline model and improved model; (b) comparison between BaseDCNN model and improved model.
(a)(b)In Figure16(a), the recognition rates of negative emotions such as fear, disgust, and anger are 35.14%, 53.75%, and 66.67%, respectively, in the baseline model. In the model recognition results, the accuracy of these three expressions is improved by 25.66%, 2.5%, and 9.87%, respectively. The overall recognition accuracy has been significantly improved. The recognition effect of negative emotions is enhanced, which shows that the divided revolution structure used in the model design reduces the pooling operation, increases the depth of the model, and improves the recognition performance of the model. In Figure 16(b), combined with the divided revolution operation after the BaseDCNN model is enhanced by using the front and rear end method, the overall recognition accuracy is improved from 63.61% to 69.94%, which proves the effectiveness of the improved process.


### 3.2. Analysis of Structural Model Results

The reliability analysis and factor analysis of SPSS software are used to verify the validity and reliability of the questionnaire. The KMO value of the questionnaire is 0.97. Table2 shows the reliability results of the nine dimensions of the second part of the Cronbach’s α test.Table 2
Cronbach’sα of each dimension of the questionnaire.
ProjectCronbach’sαLearning motivation0.66Comprehension and understanding0.75Memorization0.71Learning transfer ability0.84Teamwork ability0.78Problem-solving skills0.55Critical thinking0.86Evaluation and reflection skills0.66Creative thinking0.86In Table1, the Cronbach α for specific questions of the questionnaire is 0.66, 0.75, 0.71, 0.84, 0.78, 0.55, 0.86, 0.66, and 0.86, respectively. The result of factor analysis showed that the structural validity of the questionnaire is 75.2%. This shows that the questionnaire has good validity and reliability and reflects the specific situation of college students in the study area based on deep learning.The fitting index result of the initial model and the fitting result of the revised structural model are shown in Figure17.Figure 17
The fitting index result of the model. (a) The fitting index result of the initial model and (b) the fitting index result of the revised model.
(a)(b)Both before and after the model correction, the fitting indexes meet the requirements of the evaluation standard. It is assumed that there is a good match between the model and the survey data.According to the relevant test results of the measurement model, the hypothetical path is proposed and tested. The test criteria mainly include parameter significance test (C.R. > 1.96,P<0.05) and rationality test (S.E. > 0), as shown in Table 3.Table 3
Parameter test values of the structural model.
HypothesisHypothetical pathS.E.C.R.βSignificancePVerification resultsJ1Learning motivation ⟶ memorization ability0.04213.4220.650.0003EstablishedJ2Ability to memorize ⟶ comprehension0.04313.3520.590.0001EstablishedJ3Comprehension ⟶ transfer learning ability0.05815.7020.790.0006EstablishedJ4Transfer learning ability ⟶ problem-solving ability0.0398.0230.310.0005EstablishedJ5Transfer learning ability ⟶ evaluation and reflection ability0.0485.2660.240.013EstablishedJ6Evaluation and reflection ⟶ critical thinking0.40−2.514−0.180.012Not establishedJ7Evaluation and reflection ability ⟶ innovative thinking0.064−2.244−0.140.025Not establishedJ8Cooperation ability ⟶ learning motivation0.03217.5340.60.0005EstablishedJ9Cooperation ability ⟶ memorization ability0.0288.2270.280.0007EstablishedJ10Cooperation ability ⟶ memory ability0.02812.6080.420.0009EstablishedJ11Cooperation ability ⟶ transfer learning ability0.04414.1900.170.0006EstablishedJ12Cooperation ability ⟶ problem-solving ability0.04415.5600.680.0007EstablishedJ13Cooperation ability ⟶ evaluation and reflection ability0.05413.3190.680.0005EstablishedJ14Cooperative ability ⟶ critical thinking0.05311.8091.070.0006EstablishedJ15Cooperation ability ⟶ innovative thinking0.07414.7701.010.0006EstablishedIn Table3, S.E. is the standard deviation. Assume that 1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 13, 14, and 15 all meet the significance test. But suppose that the parameter value C.R. of 6 is −2.514, P is 0.012, and ß is −0.18. Assume that the parameter value of 7 is −2.244 for C.R., 0.025 for P, and −0.14 for ß. The correlation coefficient C.R. and path coefficient ß of J6 and J7 are less than 0. This means that the independent variable negatively influences the dependent variable, and the test result is contrary to the null hypothesis. Therefore, the assumptions J6 and J7 do not hold.Therefore, assumptions J6 and J7 in the model are deleted. The parameters of the model are deleted and reevaluated. In Figure17(b), all indicators meet the requirements. The corrected structural model parameters, path coefficients of potential variables, and verification results are shown in Table 4.Table 4
Parameter test values of the modified structural model.
HypothesisHypothetical pathS.E.C.R.βSignificant PValidation resultsJ1Learning motivation ⟶ memorization ability0.04213.4220.650.0003EstablishedJ2Ability to memorize ⟶ comprehension and understanding0.04313.3520.590.0001EstablishedJ3Comprehension and understanding ability ⟶ transfer learning ability0.05815.7020.790.0006EstablishedJ4Transfer learning ability ⟶ problem-solving ability0.0398.0230.310.0005EstablishedJ5Transfer learning ability ⟶ evaluation and reflection ability0.0485.2660.240.013EstablishedJ8Cooperative ability ⟶ learning motivation0.03217.5340.60.0005EstablishedJ9Cooperation ability ⟶ memorization ability0.0288.2270.280.0007EstablishedJ10Cooperation ability ⟶ comprehension and understanding ability0.02812.6080.420.0009EstablishedJ11Cooperation ability ⟶ transfer learning ability0.04414.1900.170.0006EstablishedJ12Cooperation ability ⟶ problem-solving ability0.04415.5600.680.0007EstablishedJ13Cooperation ability ⟶ evaluation and reflection ability0.05413.3190.680.0005EstablishedJ14Cooperative ability ⟶ critical thinking0.05311.8091.070.0006EstablishedJ15Cooperation ability ⟶ innovative thinking0.07414.7701.010.0006EstablishedThet-test result is P<0.05, that is, the generally significant hypothesis includes. Suppose the P value of 5 is 0.013, the P value of 6 is 0.012, and the P value of 7 is 0.025. The hypothesis with P value <0.001 includes assumption 1∼hypothesis 4 and hypothesis 8∼hypothesis 15. Among the P values of the adjusted model using Figure 16, the P value of hypothesis 5 is 0.013, which is a general significance hypothesis. The P value of other hypotheses is less than 0.001, which is a very significant hypothesis.The newly designed SPE DL structural equation modeling is shown in Figure18.Figure 18
DL SEM of SPE.Figure18 shows that all the model assumptions in the figure are valid, indicating a significant structural relationship between the influencing factors of the DL process of SPE of college students in the study area. There are two ways to realize DL: learning motivation, memorization ability, understanding and comprehension ability, learning transferability, evaluation, and reflection ability; the other is learning motivation, memorization ability, knowledge and awareness ability, learning transferability, and problem-solving ability. The students lack innovative thinking and critical thinking in their DL process.


### 3.3. Analysis of the Design Results of Online Courses of SPE

The critical thinking and innovative thinking training model are shown in Figure19.Figure 19
The training model for online courses of advanced thinking.A higher-order thinking online course training model is constructed by the foregoing content. Figure19 shows that the model aims to improve students’ innovative thinking and critical thinking. The online learning platform described later will use Figure 19 as the theoretical basis.


### 3.4. Analysis of System Results of Online Learning Platform

The main page of the online learning platform is shown in Figure20.Figure 20
The main page of the learning platform.Figure20 is the main page of the constructed social science online learning platform. The main content of the built learning platform includes online courses, recommendation systems, and learning plans. The learner can find this page after successfully logging in. According to their specific circumstances, the registrant can choose a detailed study plan to complete the learning of social science knowledge finally.The connection between entities in the online learning experiment platform is shown in Figure21.Figure 21
The physical connection diagram of the experimental platform.Figure21 shows that there are connections between entities. Resource libraries, students, behavior records, and learning forums are the most important parts of the entities. And they are managed uniformly by the administrator.The reliability and validity results of the questionnaire in the platform experiment are good. The platform performance test results are given, including the comparison chart of the experimental and control groups, the comparison chart before and after the control group, the comparison chart before and after the experimental group, and the comparison between the experimental group and the experimental group. The comparison chart of the control group results is shown in Figure22.Figure 22
Comparison of the effects of SPE on online platforms. (a–d) The comparison of the experimental group and the control group before and after the test data, the control group before and after the test data comparison, the experimental group before and after the test data comparison, and posttest data comparison between the experimental group and the control group.
(a)(b)(c)(d)Figure22 shows that the pretest data of the experimental group is 3.85, and the pretest data of the control group is 3.84, indicating that the levels of innovative thinking and critical thinking tendencies of the two are consistent. From the overall dimension of the data before and after the control group, the difference is 0.01, which shows that the difference between the students in using other social and political learning methods is not significant. The experimental group’s pretest and posttest data showed significant differences. The pretest data is 3.85, and the posttest information is 4.07, indicating that the experimental platform design played a positive role. The comparison of the posttest data of the two groups showed apparent differences, which further indicates that the online course learning model constructed has a positive effect on students’ innovative thinking and critical thinking.


### 3.5. The Influence of Gender on Social Science Ability

The difference analysis results of different genders under the social science learning system based on deep learning technology are shown in Table5.Table 5
Results of the effect of gender differences on social science learning ability.
Influencing factorsLevene’s testGendertSig.FSig.FemaleMaleLearning motivationAssuming the equations are equal0.270.603.69 ± 0.673.63 ± 0.652.010.04MemorizationAssuming the equations are equal0.250.623.68 ± 0.633.64 ± 0.631.340.18Comprehension and understandingAssuming the equations are equal1.960.163.39 ± 0.673.37 ± 0.640.560.57Transfer learning abilityAssuming the equations are equal0.270.603.41 ± 0.683.33 ± 0.682.240.03Problem-solving skillsAssuming the equations are equal3.010.083.34 ± 0.613.32 ± 0.580.830.41Teamwork abilityAssuming the equations are equal0.280.593.76 ± 0.583.71 ± 0.591.300.19Evaluation and reflection skillsAssuming the equations are equal0.000.963.44 ± 0.613.39 ± 0.611.600.11Critical thinkingAssuming the equations are equal2.030.163.24 ± 0.663.20 ± 0.651.290.20Creative thinkingAssuming the equations are equal0.000.943.18 ± 0.703.14 ± 0.701.320.20In Table5, before the independent sample t is tested, the data is subjected to the Levene test of the variance equation to verify whether the variances of the two groups of male college students and female college students are equal. The Sig. values in the variance test of each dimension are all greater than 0.05, indicating that the hypothesis of equal variances is established. Next, the Sig. value in the t-test is observed. The Sig. values of learning motivation and transfer learning ability are 0.04 and 0.03, respectively, which are both less than 0.05. It shows significant differences in learning motivation and transfer learning ability between male college students and teenage college students. The t-values of these two dimensions are 2.01 and 2.24, respectively, indicating that female college students’ learning motivation and transfer learning ability are higher than those of male college students. Due to the gender unequal employment situation, women tend to have stronger learning motivation. Their transfer learning ability is also affected by high learning motivation, showing higher transfer learning ability.


## 3.1. Face Recognition Accuracy Comparison

The accuracy comparison of face recognition is shown in Figure16.Figure 16
Face recognition accuracy comparison. (a) The comparison between baseline model and improved model; (b) comparison between BaseDCNN model and improved model.
(a)(b)In Figure16(a), the recognition rates of negative emotions such as fear, disgust, and anger are 35.14%, 53.75%, and 66.67%, respectively, in the baseline model. In the model recognition results, the accuracy of these three expressions is improved by 25.66%, 2.5%, and 9.87%, respectively. The overall recognition accuracy has been significantly improved. The recognition effect of negative emotions is enhanced, which shows that the divided revolution structure used in the model design reduces the pooling operation, increases the depth of the model, and improves the recognition performance of the model. In Figure 16(b), combined with the divided revolution operation after the BaseDCNN model is enhanced by using the front and rear end method, the overall recognition accuracy is improved from 63.61% to 69.94%, which proves the effectiveness of the improved process.


## 3.2. Analysis of Structural Model Results

The reliability analysis and factor analysis of SPSS software are used to verify the validity and reliability of the questionnaire. The KMO value of the questionnaire is 0.97. Table2 shows the reliability results of the nine dimensions of the second part of the Cronbach’s α test.Table 2
Cronbach’sα of each dimension of the questionnaire.
ProjectCronbach’sαLearning motivation0.66Comprehension and understanding0.75Memorization0.71Learning transfer ability0.84Teamwork ability0.78Problem-solving skills0.55Critical thinking0.86Evaluation and reflection skills0.66Creative thinking0.86In Table1, the Cronbach α for specific questions of the questionnaire is 0.66, 0.75, 0.71, 0.84, 0.78, 0.55, 0.86, 0.66, and 0.86, respectively. The result of factor analysis showed that the structural validity of the questionnaire is 75.2%. This shows that the questionnaire has good validity and reliability and reflects the specific situation of college students in the study area based on deep learning.The fitting index result of the initial model and the fitting result of the revised structural model are shown in Figure17.Figure 17
The fitting index result of the model. (a) The fitting index result of the initial model and (b) the fitting index result of the revised model.
(a)(b)Both before and after the model correction, the fitting indexes meet the requirements of the evaluation standard. It is assumed that there is a good match between the model and the survey data.According to the relevant test results of the measurement model, the hypothetical path is proposed and tested. The test criteria mainly include parameter significance test (C.R. > 1.96,P<0.05) and rationality test (S.E. > 0), as shown in Table 3.Table 3
Parameter test values of the structural model.
HypothesisHypothetical pathS.E.C.R.βSignificancePVerification resultsJ1Learning motivation ⟶ memorization ability0.04213.4220.650.0003EstablishedJ2Ability to memorize ⟶ comprehension0.04313.3520.590.0001EstablishedJ3Comprehension ⟶ transfer learning ability0.05815.7020.790.0006EstablishedJ4Transfer learning ability ⟶ problem-solving ability0.0398.0230.310.0005EstablishedJ5Transfer learning ability ⟶ evaluation and reflection ability0.0485.2660.240.013EstablishedJ6Evaluation and reflection ⟶ critical thinking0.40−2.514−0.180.012Not establishedJ7Evaluation and reflection ability ⟶ innovative thinking0.064−2.244−0.140.025Not establishedJ8Cooperation ability ⟶ learning motivation0.03217.5340.60.0005EstablishedJ9Cooperation ability ⟶ memorization ability0.0288.2270.280.0007EstablishedJ10Cooperation ability ⟶ memory ability0.02812.6080.420.0009EstablishedJ11Cooperation ability ⟶ transfer learning ability0.04414.1900.170.0006EstablishedJ12Cooperation ability ⟶ problem-solving ability0.04415.5600.680.0007EstablishedJ13Cooperation ability ⟶ evaluation and reflection ability0.05413.3190.680.0005EstablishedJ14Cooperative ability ⟶ critical thinking0.05311.8091.070.0006EstablishedJ15Cooperation ability ⟶ innovative thinking0.07414.7701.010.0006EstablishedIn Table3, S.E. is the standard deviation. Assume that 1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 13, 14, and 15 all meet the significance test. But suppose that the parameter value C.R. of 6 is −2.514, P is 0.012, and ß is −0.18. Assume that the parameter value of 7 is −2.244 for C.R., 0.025 for P, and −0.14 for ß. The correlation coefficient C.R. and path coefficient ß of J6 and J7 are less than 0. This means that the independent variable negatively influences the dependent variable, and the test result is contrary to the null hypothesis. Therefore, the assumptions J6 and J7 do not hold.Therefore, assumptions J6 and J7 in the model are deleted. The parameters of the model are deleted and reevaluated. In Figure17(b), all indicators meet the requirements. The corrected structural model parameters, path coefficients of potential variables, and verification results are shown in Table 4.Table 4
Parameter test values of the modified structural model.
HypothesisHypothetical pathS.E.C.R.βSignificant PValidation resultsJ1Learning motivation ⟶ memorization ability0.04213.4220.650.0003EstablishedJ2Ability to memorize ⟶ comprehension and understanding0.04313.3520.590.0001EstablishedJ3Comprehension and understanding ability ⟶ transfer learning ability0.05815.7020.790.0006EstablishedJ4Transfer learning ability ⟶ problem-solving ability0.0398.0230.310.0005EstablishedJ5Transfer learning ability ⟶ evaluation and reflection ability0.0485.2660.240.013EstablishedJ8Cooperative ability ⟶ learning motivation0.03217.5340.60.0005EstablishedJ9Cooperation ability ⟶ memorization ability0.0288.2270.280.0007EstablishedJ10Cooperation ability ⟶ comprehension and understanding ability0.02812.6080.420.0009EstablishedJ11Cooperation ability ⟶ transfer learning ability0.04414.1900.170.0006EstablishedJ12Cooperation ability ⟶ problem-solving ability0.04415.5600.680.0007EstablishedJ13Cooperation ability ⟶ evaluation and reflection ability0.05413.3190.680.0005EstablishedJ14Cooperative ability ⟶ critical thinking0.05311.8091.070.0006EstablishedJ15Cooperation ability ⟶ innovative thinking0.07414.7701.010.0006EstablishedThet-test result is P<0.05, that is, the generally significant hypothesis includes. Suppose the P value of 5 is 0.013, the P value of 6 is 0.012, and the P value of 7 is 0.025. The hypothesis with P value <0.001 includes assumption 1∼hypothesis 4 and hypothesis 8∼hypothesis 15. Among the P values of the adjusted model using Figure 16, the P value of hypothesis 5 is 0.013, which is a general significance hypothesis. The P value of other hypotheses is less than 0.001, which is a very significant hypothesis.The newly designed SPE DL structural equation modeling is shown in Figure18.Figure 18
DL SEM of SPE.Figure18 shows that all the model assumptions in the figure are valid, indicating a significant structural relationship between the influencing factors of the DL process of SPE of college students in the study area. There are two ways to realize DL: learning motivation, memorization ability, understanding and comprehension ability, learning transferability, evaluation, and reflection ability; the other is learning motivation, memorization ability, knowledge and awareness ability, learning transferability, and problem-solving ability. The students lack innovative thinking and critical thinking in their DL process.


## 3.3. Analysis of the Design Results of Online Courses of SPE

The critical thinking and innovative thinking training model are shown in Figure19.Figure 19
The training model for online courses of advanced thinking.A higher-order thinking online course training model is constructed by the foregoing content. Figure19 shows that the model aims to improve students’ innovative thinking and critical thinking. The online learning platform described later will use Figure 19 as the theoretical basis.


## 3.4. Analysis of System Results of Online Learning Platform

The main page of the online learning platform is shown in Figure20.Figure 20
The main page of the learning platform.Figure20 is the main page of the constructed social science online learning platform. The main content of the built learning platform includes online courses, recommendation systems, and learning plans. The learner can find this page after successfully logging in. According to their specific circumstances, the registrant can choose a detailed study plan to complete the learning of social science knowledge finally.The connection between entities in the online learning experiment platform is shown in Figure21.Figure 21
The physical connection diagram of the experimental platform.Figure21 shows that there are connections between entities. Resource libraries, students, behavior records, and learning forums are the most important parts of the entities. And they are managed uniformly by the administrator.The reliability and validity results of the questionnaire in the platform experiment are good. The platform performance test results are given, including the comparison chart of the experimental and control groups, the comparison chart before and after the control group, the comparison chart before and after the experimental group, and the comparison between the experimental group and the experimental group. The comparison chart of the control group results is shown in Figure22.Figure 22
Comparison of the effects of SPE on online platforms. (a–d) The comparison of the experimental group and the control group before and after the test data, the control group before and after the test data comparison, the experimental group before and after the test data comparison, and posttest data comparison between the experimental group and the control group.
(a)(b)(c)(d)Figure22 shows that the pretest data of the experimental group is 3.85, and the pretest data of the control group is 3.84, indicating that the levels of innovative thinking and critical thinking tendencies of the two are consistent. From the overall dimension of the data before and after the control group, the difference is 0.01, which shows that the difference between the students in using other social and political learning methods is not significant. The experimental group’s pretest and posttest data showed significant differences. The pretest data is 3.85, and the posttest information is 4.07, indicating that the experimental platform design played a positive role. The comparison of the posttest data of the two groups showed apparent differences, which further indicates that the online course learning model constructed has a positive effect on students’ innovative thinking and critical thinking.


## 3.5. The Influence of Gender on Social Science Ability

The difference analysis results of different genders under the social science learning system based on deep learning technology are shown in Table5.Table 5
Results of the effect of gender differences on social science learning ability.
Influencing factorsLevene’s testGendertSig.FSig.FemaleMaleLearning motivationAssuming the equations are equal0.270.603.69 ± 0.673.63 ± 0.652.010.04MemorizationAssuming the equations are equal0.250.623.68 ± 0.633.64 ± 0.631.340.18Comprehension and understandingAssuming the equations are equal1.960.163.39 ± 0.673.37 ± 0.640.560.57Transfer learning abilityAssuming the equations are equal0.270.603.41 ± 0.683.33 ± 0.682.240.03Problem-solving skillsAssuming the equations are equal3.010.083.34 ± 0.613.32 ± 0.580.830.41Teamwork abilityAssuming the equations are equal0.280.593.76 ± 0.583.71 ± 0.591.300.19Evaluation and reflection skillsAssuming the equations are equal0.000.963.44 ± 0.613.39 ± 0.611.600.11Critical thinkingAssuming the equations are equal2.030.163.24 ± 0.663.20 ± 0.651.290.20Creative thinkingAssuming the equations are equal0.000.943.18 ± 0.703.14 ± 0.701.320.20In Table5, before the independent sample t is tested, the data is subjected to the Levene test of the variance equation to verify whether the variances of the two groups of male college students and female college students are equal. The Sig. values in the variance test of each dimension are all greater than 0.05, indicating that the hypothesis of equal variances is established. Next, the Sig. value in the t-test is observed. The Sig. values of learning motivation and transfer learning ability are 0.04 and 0.03, respectively, which are both less than 0.05. It shows significant differences in learning motivation and transfer learning ability between male college students and teenage college students. The t-values of these two dimensions are 2.01 and 2.24, respectively, indicating that female college students’ learning motivation and transfer learning ability are higher than those of male college students. Due to the gender unequal employment situation, women tend to have stronger learning motivation. Their transfer learning ability is also affected by high learning motivation, showing higher transfer learning ability.


## 4. Discussion

The emotion recognition rate of the CNN model constructed based on the hole rate theory is improved by about 9%. The accuracy is lower than Naveen and Sivakumar’s research results on CNN-based pose-invariant face recognition. Compared with the new adaptive morphological bilateral filter used to study the consequences of face recognition under an unknown light environment, the accuracy here is within an acceptable range, which is higher than that of traditional face recognition [40]. The influencing factors of the learning process are determined through the literary analysis of the SPE system of college students. Compared with the experimental conclusions of Kim and Kutscher on the influencing factors of learning and development of college students with disabilities, the influencing factors add more specific elements based on the environment and student characteristics [41]. Corresponding hypothetical paths are proposed combined with Bloom’s taxonomy of educational goals. The model is verified and revised through a theoretical model suitable for the learning factors of the SPE system of college students. By observing the path of the theoretical model, college students lack the development of advanced thinking such as critical thinking and innovative thinking in the learning process. This provides the basis for the design of e-learning courses. This conclusion is consistent with Thomas and Hayes’s conclusion on the relationship between critical openness and reflective skepticism. That is, college students in Eastern cultures lack a critical spirit [42]. The e-learning course aims to improve the learning motivation, transfer learning ability, cognitive basis, and cultural characteristics of college students’ e-learning, providing a theoretical and practical foundation for this research. Based on this, the online model is constructed. Through the in-depth analysis of the learning environment, learning resources, and learning recommendation system of the online learning experiment platform, an online learning experiment platform for dynamic learning and higher-order thinking development is designed and developed. The platform uses a face recognition module based on deep learning technology. The platform test verifies the stability and effectiveness of the forum. This provides a way and a platform to promote the development of college students’ higher-order thinking in e-learning, making the research more complete and practical. Finally, based on deep learning techniques, the social science learning ability under different genders is analyzed. There are significant differences between women and men in learning motivation and learning transfer. That is, female college students have higher learning motivation than male college students. The transfer learning ability of female college students is more robust than that of male college students.


## 5. Conclusions

In the current network teaching, teachers emphasize the dissemination of knowledge and the ability of students to master knowledge. The student’s request is only to fully accept the knowledge imparted by the teacher, without the need for the student’s critical thinking. This is not conducive to cultivating students’ high-order thinking. Therefore, college students are selected as the research subjects. A hypothetical model of the dynamic learning process of college students is constructed and empirically studied. Online learning courses are designed and built. This promotes the development of an online learning model for college students’ critical and innovative thinking. Finally, an experimental platform for online learning is developed, including a CNN-based face recognition module. The results show that the constructed learning model has a certain positive effect. The researched content has a certain universality, which provides a new reference for the development of network teaching and is applied to social science education in colleges and universities. The disadvantage is that the data set in the application of deep learning technology is relatively single. In the research process, the influence of innovative thinking and critical thinking on advanced review has certain limitations. With the development of technology and the increasing needs of learners, online course learning platforms have had a more significant impact on learners’ learning. The online course learning experiment platform can improve the online learning experience of learners. This has a particular effect on the innovation and development of the online course learning experimental platform. In the future, datasets need to be more prosperous and more balanced. Deep learning algorithms are data-driven, and good models can only be trained with good data. The existing datasets are mainly from Western countries, and there is a data imbalance problem. In future work, data sets should be established according to actual application scenarios, which have the characteristics of faces in different countries and regions and the balance of data in various categories. In the construction of the learning platform, the learners’ learning style, cognitive preferences, and other characteristics will be taken into account, and they will be put into large-scale and long-term practice. The relevant functions of the platform will be continuously improved. A learning system that can be widely used in social science education in colleges and universities will be constructed.



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*Source: 7653766-2022-04-19.xml*