# Analysis of the Innovation Path of Marxism Popularization Based on Big Data Analysis

Authors: Dongwei Chu; Dahuai Yu
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

Marxist theory points out that practice is the method of understanding the world, and it is also the essential characteristic of human beings. Based on the above theoretical basis and guidance, this paper, from the perspective of ideological and political education evaluation, conducts research on student behavior in Marxist popular classrooms, grasps the characteristics of student behavior in Marxist popular classrooms in the new era, and explores behavioral research. Approaches and methods for ideological and political education evaluation and student evaluation were introduced. This paper analyzes the combination method of AHP and BP neural network in the previous research and points out its limitations. On this basis, it innovatively proposes an inherited combination method of AHP and BP neural network and conducts model training and exact match experiment based on AHP and BP neural network. Finally, by comparing and analyzing the above four sets of experimental results, it is found that the inheritance-type combination method of AHP-BP neural network not only avoids the problem of small AHP capacity but also scientifically integrates hierarchical weights with theoretical basis into the calculation of BP neural network. In addition, the inheritance-type combination of AHP-BP neural network in this study can replace the normalization of data and effectively improve the training speed and matching accuracy of the precise matching model for political classrooms in colleges and universities under the new situation.


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## Body

## 1. Introduction

A new wave of technology is ushering in the dawn of a new era. Various information resource databases have been established one after another. Massive digital resources have pushed human society into the era of big data. Big data is an objective trend in the development of the information age and a new stage in the development of science and technology [1]. General Secretary Xi Jinping pointed out: “With the integration of information technology and human production and life, and the rapid popularization of the Internet, global data is characterized by explosive growth and massive agglomeration, which has had a significant impact on economic development, social governance, national management, and people’s lives.” The popularization of Marxism is a vivid manifestation of the distinctive character of Marxism, and it is also a key move to maintain our ideological position. Therefore, exploring how to use big data technology to promote the popularization of Marxism is not only an inherent requirement to consolidate the guiding position of Marxism in the ideological field of our country but also an inevitable requirement to realize the eternal vitality of Marxism [2].The popularization of Marxism is an important way to consolidate the guiding position of Marxism in the ideological field of our country, and it is also a key link to ensure the people’s firm belief in Marxism [3]. With the wide application of big data in various fields of society, it has demonstrated its important role in human social practice [4]. Big data has shaken up every aspect of the world, from business technology to healthcare, government, education, economics, humanities, and every other area of society. As a result, the popularization of Marxism based on big data technology has also ushered in new opportunities for development [5]. With the development of the times, emerging media has the characteristics of cross time, cross channel, and expansibility. It can sort out netizens’ SMS, emails, and website posts at the first time, update conference and communication information in time, optimize and develop the popularization and Sinicization of Marxism, and solve the practical and theoretical problems raised by the masses. In the era of big data, the upgrading of information dissemination has made the theoretical exchange of Marxism more convenient and the channels of information extension more diversified, creating an environment for the modernization of Marxism.Although the traditional Marxist mass media has made important contributions in history, since entering the era of big data, the traditional media represented by newspapers, radio, and television are faced with the relative lag in the content of communication, the one-way mode of communication, and realistic dilemmas such as the difficulty of evaluating the communication effect. On the other hand, in the context of the era of big data, various media are correspondingly updated according to the development trend of the times, which presents a picture of diversified information, resource sharing, and subject diversification [6]. As a communication medium derived from big data technology, online video has increasingly become the main way for people to obtain information. According to the 49th “Statistical Report on Internet Development in China” issued by the China Internet Network Information Center (CNNIC), as of December 2021, the number of netizens in my country has reached 1.032 billion, of which the number of online video users is 975 million [7]. Since entering the era of big data, public opinion has become loose, the spread of Marxism has increased, and it has begun to change from passive to interactive. Ordinary people use various new media to actively participate in exchanges, set up online communities, microblogs, WeChat, and offline and online exchanges, and successfully become the main body of communication, with the initiative to evaluate and disseminate information.In recent years, video works such as “Marx is Right” and “Leaders of the Wind” have gradually become emerging media for the dissemination of Marxist theory. They present Marxist theory in a form that the people like to hear through texts, pictures, and audio. In order to vividly visualize abstract and complex knowledge, people’s original stereotype that Marxist theory is obscure and difficult to understand [8]. In addition, most video works are 5-20 minutes long, so that people can use fragmented time to watch and learn. The rational elaboration and interactive communication of the theory through online video provides a new path choice for the public to approach Marx, which undoubtedly has important practical significance for promoting the popularization of Marxism [9].


## 2. State of the Art

In recent years, the domestic academic circles have carried out extensive discussions on the popularization of Marxist classrooms in colleges and universities, and the related research results are relatively abundant. With the help of rich literature, it is found that in the early stage of the research on the popularization of Marxism classroom in colleges and universities, it mainly focuses on the connotation and positioning of the popularization of Marxism classroom in colleges and universities, the function and role of the popularization of Marxism classroom in colleges and universities, the educational mode of the popularization of Marxism classroom in colleges and universities, the problems and countermeasures existing in the popularization of Marxism classroom in colleges and universities, the “report card of the popularization of Marxism classroom” in colleges and universities, and evaluation of teaching effect of Marxism popularization classroom education in colleges and universities [10].First is the research on the connotation of Marxist popular classroom in colleges and universities. Domestic academic circles have conducted a lot of research on the connotation of the Marxist popular classroom in colleges and universities. Through systematic review, it is found that scholars mainly define the connotation of the Marxist popular classroom in colleges and universities from the theoretical logic of the first classroom and the traditional classification of the Marxist popular classroom, which are all in line with higher education [11]. The more representative viewpoints are as follows: Huang et al. believe that the Marxist popular classroom means that schools guide and organize students to carry out various extracurricular educational activities outside the teaching plan [12]. Olszak and Mach-Król believe that the Marxist popular classroom plays an important role in extracurricular education and teaching activities. Every extracurricular activity should be planned and organized in advance, so that students’ abilities can be improved, and they must be nurtured and obtain knowledge in a pleasant atmosphere [13]. Hu and Bajorath expounded on the popularization of Marxist classrooms in colleges and universities and believed that the so-called “second” was carried out outside the classroom relative to the “first” [14]. The so-called “classroom” shows that the Marxist popular classroom is not the pure spare time of students, but an organized and collective education. Based on the opinions of domestic scholars, although the definitions of the Marxist popular classroom in colleges and universities are different, the essential connotation is not very different. From the perspective of the scope of the activities of the Marxist popular classroom in colleges and universities, there are mainly the following two views: the first view refers to the teaching set by the school. Activities are not planned. Another point of view is to understand it from a broader perspective, that is, all practical activities inside and outside the classroom [15].Second is the research on the orientation of Marxist popular classroom in colleges and universities. The precise positioning of the Marxist popular classroom is the prerequisite for the development of the Marxist popular classroom in colleges and universities. The research on the positioning of the Marxist popular classroom mainly focuses on the nature of the Marxist popular classroom activities, the content of the activities, and the roles of teachers and students [16]. The content of the activities serves the classroom and is conducive to strengthening the content of the classroom; the content of the Marxist popular classroom activities is a supplement to the classroom and plays a role in making up for the lack [17]. On the positioning of the roles of teachers and students in the Marxist popular classroom, the roles of teachers and students are divided into three stages, in which teachers are leading, guiding, and tutoring and students are passive, interactive, and active [18]. At the same time, Aslam et al. also reflected on the positioning of Marxist popular classrooms in colleges and universities and pointed out that the traditional relationship positioning of “Marxist popular classrooms are the extension of the first classroom” limits the value of Marxist popular classrooms. Grasp with both hands [19].


## 3. Methodology

### 3.1. Evaluation Theory of Ideological and Political Education

The evaluation of the effect of Ideological and political education is an important part of ideological and political education activities. Through the evaluation of the effect of ideological and political education, we can accurately understand the situation of the previous ideological and political education, timely make realistic and accurate information feedback on all links of ideological and political education, better understand the progress of ideological and political education, and further grasp the laws of ideological and political education, in order to promote the scientific decision-making of ideological and political education and realize the scientization of ideological and political education. The evaluation of ideological and political education, an educational activity, has formed two levels, one is the cognitive evaluation of its ontological characteristics, and the other is the evaluation of its impact, that is, the evaluation of its educational results [20]. In the cognitive evaluation, the concept of what kind of ideological and political education should be established first is in line with the standard. There are many bases for this process. The theory of ideological and political education has clear requirements and guidance for the content, form, and method of ideological and political education [21]. And this kind of evaluation relies more on objective conditions such as system, investment, personnel, and venue to measure, which is clear at a glance. However, the evaluation of work is theoretically an objective cognitive evaluation. In practice, it is difficult to achieve this because if there is no measurement of results, the subjectivity of the standard will not be limited. In fact, in the current evaluation of ideological and political work in colleges and universities, it has appeared that “doing a good job is not as good as doing a PPT,” “being loved by students, not as good as a paper,” “with the same joys and hardships as students, not as good as the gold award in student competition,” etc. are not conducive to ideological and political education and education. Therefore, only the evaluation of work cannot cover the full connotation and requirements of the evaluation of ideological and political work, and the evaluation of ideological and political results is the theme of this research [22]. First, we must understand several elements that influence this evaluation. The relationship between the process and results of Marxist popular classroom education is an important basis for the study of evaluation theory. In ideological and political education, student behavior in Marxist popular classrooms, student thinking, comprehensive quality, and student growth and development have formed a relationship of mutual influence and mutual restriction. We use Figure 1 to show the relationship between these phenomena and things.Figure 1
Logic diagram 1 of the correlation relationship between Marxist popular classroom behavior.Figure1 shows that the purpose and significance of ideological and political education is to change students’ thoughts and behaviors. This is an effective education field proved by the history and practice of my country’s higher education development, and it is also the characteristics and experience of my country’s higher education. The starting point and end point of “guiding thought and regulating behavior” are students’ thoughts and behaviors, which are determined by basic theories such as the content, methods, and laws of ideological and political education. Therefore, ideological and political education has an inevitable causal relationship with behavior and thought.


### 3.2. Overview of BP Neural Network

BP (back propagation) neural network is a multilayer feedforward neural network trained according to the error back propagation algorithm. It is a relatively basic artificial neural network. Initially, all edge weights were assigned randomly. For all the inputs in the training data set, the artificial neural network is activated and its output is observed. These outputs will be compared with our known and expected outputs, and the error will “propagate” back to the next level. The error will be marked and the weight will be “adjusted” accordingly. The process is repeated until the output error is lower than the established standard. Usually, the following empirical formula is used:(1)h=m+n+a.h represents the number of nodes in the hidden layer, m represents the number of nodes in the input layer, n represents the number of nodes in the output layer, and a is an adjustment constant usually ranging from 1 to 10.The specific operation process of BP neural network is divided into two parts: (1) the forward propagation process of the input signal and (2) the back propagation process of the error signal. It is assumed that the input layer of the BP neural network hasn neurons, the hidden layer has m neurons, and the output layer has q neurons.When the input data enters the input layer, the weights and thresholds are connected to the layer for calculation as shown in formula (2).The following is the process to get the input data as the hidden layer. For the hidden layer, its output is determined by the activation function, and the result shown in equation (3) can be obtained by substituting a into the activation function.
(2)ak1=∑i=1nwki1xi−bk1,(3)zk1=fak1=f∑i=1nwki1xi−bk1.In the output layer, the output data of the hidden layer and the corresponding connection weights and thresholds are calculated as shown in formula (4).As the input data of the output layer, the output data of the output layer is also determined by substituting the input data into the activation function, as shown in formula (5).
(4)aj2=∑k=1mwjk2zk1−bj2,(5)zj2=faj2=f∑k=1mwjk2zk1−bj2.When the results in the above process do not match the expected results, the process of back propagation of the error signal will be entered. LetZ be the expected result; there is an error E between the output result of the forward propagation process and the expected result, and its function is defined as
(6)E=12Z−z2=12∑j=1qZj−zj22. is the square of the difference between the actual output and the expected output of the output layer node j. Calculate the output error value of the neurons in each layer of the BP neural network.The output layer error signal is defined as(7)ej2=−∂E∂aj2.Order(8)ej2=−∂E∂aj2=−∂E∂zj2∂zj2∂aj2,because(9)zj2=faj2,∂E∂aj2=−zj−zj2.So(10)ej2=−∂E∂zj2f′aj2=Zj−zj2zj21−zj2.The hidden layer error signal is defined as(11)ek1=−∂E∂ak1.Order(12)ek1=−∂E∂ak1=−∂E∂zk1∂zk1∂ak1,because(13)∂E∂zk1=∂∂zk112∑j=1qzj−zj22=−∑j=1qzj−zj2∂zj2∂zk1.And because(14)∂zj2∂zk1=∂zj2∂aj2∂aj2∂zk1=f′aj2∂aj2∂zk1.So(15)ej2=Zj−zj2f′aj2,(16)∂aj2∂zk1=wjk2.Therefore,(17)∂E∂zk1=−∑j=1qZj−zj2f′aj2∂aj2∂zk1=−∑j=1qej2wjk2.Because(18)∂zk1∂ak1=f′ak1.It can be obtained that the error signal of the hidden layer is(19)ek1=∑j=1qej2wjk2f′ak1=∑j=1qej2wjk2zk11−zk1.After obtaining the error between the result in the neural network and the expected result, the gradient descent method needs to be used to continuously reduce the error by adjusting the weight of the output layer and the hidden layer to improve the calculation accuracy of the BP neural network, and the weights are adjusted in the direction of the negative gradient, so the amount of weight adjustment is proportional to the negative gradient of the error.


### 3.3. Introduction of AHP Method

AHP is often used to solve complex multiobjective, multilevel unstructured decision-making problems. The idea of solving the problem is as follows: firstly, decompose the overall goal to be achieved by complex problems into multiple components, so that the overall goal can be divided into multiple subgoals. Then, arrive at criteria or alternatives to achieve subgoals so that complex problems can be decomposed into systems with multiple hierarchies. Finally, solve complex problems according to the importance of the underlying criteria or alternatives to the overall goal of the high-level or the ranking of their pros and cons.AHP has the advantages of strong systematicness, simplicity and practicality, and less quantitative data required. AHP emphasizes the user’s understanding of the nature and elements of complex problems without strong mathematical theory. Get clear, simple, unambiguous results with simple comparisons and math. This analysis method is particularly outstanding in solving unstructured problems and complex problems with multiobjective and multicriteria and can solve many practical problems that are difficult to solve by traditional optimization techniques. When people systematically analyze the problems in the fields of society, economy, and management, they often face a complex system composed of many interrelated and mutually restrictive factors. Analytic hierarchy process provides a new, concise, and practical decision-making method for studying this kind of complex system.At the same time, AHP also has many shortcomings, such as being too subjective, not very creative, and not too many indicators. It is precisely because AHP overemphasizes the user’s understanding of the nature and elements of the problem that many studies question the scientificity of the algorithm. At the same time, the function of the algorithm is to compare the given factors and select the best one, so the formation of the final result will not consider other data other than artificially set factors, which makes AHP limited and lack creativity. In addition, when AHP faces a large number of indicators, we need to construct a larger judgment matrix, and the scale of the judgment matrix should change with the increase of the number of indicators. This makes the algorithm need to perform when faced with multiple indicators. Huge data statistics work and it is difficult to ensure the consistency of weight distribution.


## 3.1. Evaluation Theory of Ideological and Political Education

The evaluation of the effect of Ideological and political education is an important part of ideological and political education activities. Through the evaluation of the effect of ideological and political education, we can accurately understand the situation of the previous ideological and political education, timely make realistic and accurate information feedback on all links of ideological and political education, better understand the progress of ideological and political education, and further grasp the laws of ideological and political education, in order to promote the scientific decision-making of ideological and political education and realize the scientization of ideological and political education. The evaluation of ideological and political education, an educational activity, has formed two levels, one is the cognitive evaluation of its ontological characteristics, and the other is the evaluation of its impact, that is, the evaluation of its educational results [20]. In the cognitive evaluation, the concept of what kind of ideological and political education should be established first is in line with the standard. There are many bases for this process. The theory of ideological and political education has clear requirements and guidance for the content, form, and method of ideological and political education [21]. And this kind of evaluation relies more on objective conditions such as system, investment, personnel, and venue to measure, which is clear at a glance. However, the evaluation of work is theoretically an objective cognitive evaluation. In practice, it is difficult to achieve this because if there is no measurement of results, the subjectivity of the standard will not be limited. In fact, in the current evaluation of ideological and political work in colleges and universities, it has appeared that “doing a good job is not as good as doing a PPT,” “being loved by students, not as good as a paper,” “with the same joys and hardships as students, not as good as the gold award in student competition,” etc. are not conducive to ideological and political education and education. Therefore, only the evaluation of work cannot cover the full connotation and requirements of the evaluation of ideological and political work, and the evaluation of ideological and political results is the theme of this research [22]. First, we must understand several elements that influence this evaluation. The relationship between the process and results of Marxist popular classroom education is an important basis for the study of evaluation theory. In ideological and political education, student behavior in Marxist popular classrooms, student thinking, comprehensive quality, and student growth and development have formed a relationship of mutual influence and mutual restriction. We use Figure 1 to show the relationship between these phenomena and things.Figure 1
Logic diagram 1 of the correlation relationship between Marxist popular classroom behavior.Figure1 shows that the purpose and significance of ideological and political education is to change students’ thoughts and behaviors. This is an effective education field proved by the history and practice of my country’s higher education development, and it is also the characteristics and experience of my country’s higher education. The starting point and end point of “guiding thought and regulating behavior” are students’ thoughts and behaviors, which are determined by basic theories such as the content, methods, and laws of ideological and political education. Therefore, ideological and political education has an inevitable causal relationship with behavior and thought.


## 3.2. Overview of BP Neural Network

BP (back propagation) neural network is a multilayer feedforward neural network trained according to the error back propagation algorithm. It is a relatively basic artificial neural network. Initially, all edge weights were assigned randomly. For all the inputs in the training data set, the artificial neural network is activated and its output is observed. These outputs will be compared with our known and expected outputs, and the error will “propagate” back to the next level. The error will be marked and the weight will be “adjusted” accordingly. The process is repeated until the output error is lower than the established standard. Usually, the following empirical formula is used:(1)h=m+n+a.h represents the number of nodes in the hidden layer, m represents the number of nodes in the input layer, n represents the number of nodes in the output layer, and a is an adjustment constant usually ranging from 1 to 10.The specific operation process of BP neural network is divided into two parts: (1) the forward propagation process of the input signal and (2) the back propagation process of the error signal. It is assumed that the input layer of the BP neural network hasn neurons, the hidden layer has m neurons, and the output layer has q neurons.When the input data enters the input layer, the weights and thresholds are connected to the layer for calculation as shown in formula (2).The following is the process to get the input data as the hidden layer. For the hidden layer, its output is determined by the activation function, and the result shown in equation (3) can be obtained by substituting a into the activation function.
(2)ak1=∑i=1nwki1xi−bk1,(3)zk1=fak1=f∑i=1nwki1xi−bk1.In the output layer, the output data of the hidden layer and the corresponding connection weights and thresholds are calculated as shown in formula (4).As the input data of the output layer, the output data of the output layer is also determined by substituting the input data into the activation function, as shown in formula (5).
(4)aj2=∑k=1mwjk2zk1−bj2,(5)zj2=faj2=f∑k=1mwjk2zk1−bj2.When the results in the above process do not match the expected results, the process of back propagation of the error signal will be entered. LetZ be the expected result; there is an error E between the output result of the forward propagation process and the expected result, and its function is defined as
(6)E=12Z−z2=12∑j=1qZj−zj22. is the square of the difference between the actual output and the expected output of the output layer node j. Calculate the output error value of the neurons in each layer of the BP neural network.The output layer error signal is defined as(7)ej2=−∂E∂aj2.Order(8)ej2=−∂E∂aj2=−∂E∂zj2∂zj2∂aj2,because(9)zj2=faj2,∂E∂aj2=−zj−zj2.So(10)ej2=−∂E∂zj2f′aj2=Zj−zj2zj21−zj2.The hidden layer error signal is defined as(11)ek1=−∂E∂ak1.Order(12)ek1=−∂E∂ak1=−∂E∂zk1∂zk1∂ak1,because(13)∂E∂zk1=∂∂zk112∑j=1qzj−zj22=−∑j=1qzj−zj2∂zj2∂zk1.And because(14)∂zj2∂zk1=∂zj2∂aj2∂aj2∂zk1=f′aj2∂aj2∂zk1.So(15)ej2=Zj−zj2f′aj2,(16)∂aj2∂zk1=wjk2.Therefore,(17)∂E∂zk1=−∑j=1qZj−zj2f′aj2∂aj2∂zk1=−∑j=1qej2wjk2.Because(18)∂zk1∂ak1=f′ak1.It can be obtained that the error signal of the hidden layer is(19)ek1=∑j=1qej2wjk2f′ak1=∑j=1qej2wjk2zk11−zk1.After obtaining the error between the result in the neural network and the expected result, the gradient descent method needs to be used to continuously reduce the error by adjusting the weight of the output layer and the hidden layer to improve the calculation accuracy of the BP neural network, and the weights are adjusted in the direction of the negative gradient, so the amount of weight adjustment is proportional to the negative gradient of the error.


## 3.3. Introduction of AHP Method

AHP is often used to solve complex multiobjective, multilevel unstructured decision-making problems. The idea of solving the problem is as follows: firstly, decompose the overall goal to be achieved by complex problems into multiple components, so that the overall goal can be divided into multiple subgoals. Then, arrive at criteria or alternatives to achieve subgoals so that complex problems can be decomposed into systems with multiple hierarchies. Finally, solve complex problems according to the importance of the underlying criteria or alternatives to the overall goal of the high-level or the ranking of their pros and cons.AHP has the advantages of strong systematicness, simplicity and practicality, and less quantitative data required. AHP emphasizes the user’s understanding of the nature and elements of complex problems without strong mathematical theory. Get clear, simple, unambiguous results with simple comparisons and math. This analysis method is particularly outstanding in solving unstructured problems and complex problems with multiobjective and multicriteria and can solve many practical problems that are difficult to solve by traditional optimization techniques. When people systematically analyze the problems in the fields of society, economy, and management, they often face a complex system composed of many interrelated and mutually restrictive factors. Analytic hierarchy process provides a new, concise, and practical decision-making method for studying this kind of complex system.At the same time, AHP also has many shortcomings, such as being too subjective, not very creative, and not too many indicators. It is precisely because AHP overemphasizes the user’s understanding of the nature and elements of the problem that many studies question the scientificity of the algorithm. At the same time, the function of the algorithm is to compare the given factors and select the best one, so the formation of the final result will not consider other data other than artificially set factors, which makes AHP limited and lack creativity. In addition, when AHP faces a large number of indicators, we need to construct a larger judgment matrix, and the scale of the judgment matrix should change with the increase of the number of indicators. This makes the algorithm need to perform when faced with multiple indicators. Huge data statistics work and it is difficult to ensure the consistency of weight distribution.


## 4. Result Analysis and Discussion

### 4.1. Research on the Fusion of AHP and BP Neural Network

In the research field of person-post matching based on BP neural network, researchers found that a single model trained by BP neural network is too objective, and it is difficult to find theoretical support for the distribution of feature weights. At the same time, with the development of interdisciplinary research, researchers want to add weight distribution with theoretical basis of related disciplines to BP neural network. Therefore, with their continuous exploration and discovery, the combination of AHP and BP neural network can meet this assumption.Although this method has improved experimental results, it also has many limitations or defects:(1)
The calculation method of AHP leads to the small capacity of the scheme layer of AHP, which can usually only be used for sorting within 10 elements. Therefore, at the beginning of the research, the competency feature has to be screened, but the result will often eliminate the competency model. The outer factors and only the inner factors are considered, resulting in the limitation of the person-post matching model. At the same time, the amount of test data will be limited during the test(2)
In the combination of AHP and BP neural network, the proportional sum method also has obvious defects. Not only it is difficult to find the theoretical basis for the proportional summation method; it is even more difficult to determine in what proportion the calculation results of AHP and BP neural network are addedBased on the above analysis, it is found that although the traditional combination of AHP and BP neural network has improved the research results, it has not really achieved the researchers’ original vision. Therefore, this study proposes a new combination method—inheritance combination.The inheritance type combination of AHP and BP neural network not only avoids the problem of small AHP capacity but also integrates the artificially set initial weights with the theoretical basis of other disciplines into the calculation of BP neural network. In addition, considering that the normalization process has the problem that the sparsity is destroyed after the data is “squeezed,” the normalization of the processed data may cause the results to be very similar to the normalization results of the original data, which will play a role in “dehierarchical” effect. Therefore, when the inheritance-type combination method is adopted, there is no need to normalize the data.


### 4.2. Comparison and Analysis of Experimental Results

Since this time, the experimental part of this research has all ended, and the experimental results are as shown in Table1 after finishing:Table 1
The accuracy test results of the precise matching model of the political classroom in colleges and universities.
Original groupGroup togetherAHP not groupedAHP group togetherAccuracy50%98.4%99.59%98.4%Compared with the experimental results, it is found that the accuracy of the “original array” is at least 50%, and the normalization of the data has indeed played its advantageous role in the experiment. The “normalized group” not only shortens the training time but also improves its accuracy. to 98.4%.After combining AHP and BP neural network by inheritance, the experimental results are as follows: the accuracy of “AHP is not grouped” is 99.6%, and the accuracy of “AHP is grouped” drops to 98.4%. Combined with the previous “grouped” experimental results, it is found that(1)
in the experiment, AHP can effectively improve the accuracy of the person-post matching model by combining with the BP neural network using the inheritance method(2)
in the experiment of combining AHP and BP neural network, the normalization processing of the hierarchical data makes the accuracy of the human-post matching model fall back to the same value as the training result before the combination. The analysis shows that the reason for this phenomenon is that the normalization of the hierarchical data destroys the sparsity of the hierarchical data, resulting in the phenomenon of de-hierarchical dataTo sum up, this study concluded that “AHP is not grouped” not only integrates the competency hierarchy model related to other disciplines into the model training through AHP, which effectively improves the training speed and accuracy, but also improves the training speed and accuracy. It avoids the destructive sparsity problem of data normalization and then obtains a more scientific and more objective and practical method in the research on accurate matching of political classrooms in colleges and universities, which can effectively help colleges and universities to solve the problem of accurate matching of political classrooms in colleges and universities. The implementation of precise teaching of Ideological and political theory is the key to the implementation of precise ideological and political education in colleges and universities. The three cornerstones of artificial intelligence, algorithm, data, and computing power provide the possibility and feasibility for the precise teaching of Ideological and political theory courses in colleges and universities.


### 4.3. Statistical Analysis of Behavioral Status

In order to test which specific distribution the number of Marxist popular classroom participation obeys, we need to test it. The parametric test and the nonparametric test together constitute the content of statistical inference. The parametric test is what kind of distribution state the known data obeys. For example, theT test and the Z test all assume that the samples come from a normally distributed population, but because we do not know the frequency of students participating in popular Marxist classrooms, it is related to the numerical characteristics of probability, so we can only draw the kernel density map of the total frequency of Marxist popular classroom activities according to the idea of nonparametric statistics, as shown in Figure 2. The kernel density map is a probability graph of actual statistics, so it does not consider the mathematical probability distribution of variables, it is a direct data record, there is no prior estimation, and there is no error, and it can truthfully reflect the real situation of the data. The actual statistic is discrete, so its statistical graph is generally displayed as a histogram, and the kernel density graph is a natural extension of the calculus of the histogram to form a probability density estimation function graph.Figure 2
Kernel density map of the frequency of students participating in Marxist popular classrooms.In Figure2, the abscissa is the total frequency of students’ Marxist popular classroom, and the ordinate is the frequency of occurrence corresponding to these frequencies, that is, the proportion of students. For example, there are 41 students with 20 activities, accounting for 0.043 of the total number of samples, record all the points in the figure, and use the algorithm with the smallest error, such as the least squares method, to get the blue curve in the figure. The red curve is a theoretical normal distribution graph calculated by assuming that the total frequency distribution of students’ Marxist popular classroom activities obeys a normal distribution and calculated with the mean μ and variance σ2 obtained from the statistical data. Intuitively, the graph is close to the normal distribution. In order to further confirm, we test it for normal distribution, and its probability density function graph is shown in Figure 3:Figure 3
Normal distribution diagram.The above figure shows that one of the characteristics of the normal distribution is that the area under the probability density curve with the mean plus or minus one standard deviation covers 68.2% of the median sample size, which is of great value to educational research.The means of testing whether the probability model of data distribution is normally distributed are mainly divided into graphic observation and statistical calculation. Graphical means include Q-Q plots and P-P plots, and statistical means include the Kolmogorov-Smirnov and Shapiro-Wilk tests. SPSS23 software was used to test the normal distribution of the total frequency of students’ Marxist popular classroom activities. The results are shown in Table2 and Figures 4 and 5.Table 2
Normality test.
Kolmogorov-SmirnovShapiro-WilkN total number of second classesStatisticsDegrees of freedomSalienceStatisticsDegrees of freedomSalience.103954.000.932954.000Figure 4
The normal distribution test of the frequency of Marxist popular classroom activities.Figure 5
Histogram of the frequency of Marxist popular classroom activities.The above results show that, from the perspective of calculation and verification, the significance value of the Kolmogorov-Smirnov and Shapiro-Wilk tests is 0.000, which is a very small value, far smaller than the general statistical confidence interval of 0.05, which is the total frequency distribution of this research case. It does not obey a normal distribution.From the Q-Q graph and histogram, the measured value and the normal value curve have a certain degree of agreement. Therefore, we speculate that when the sample sizen increases infinitely, the limiting distribution is a normal distribution, that is, asymptotic normal distribution. For the asymptotic normality of the data, we have the following interpretation. For any student, the number of times he participated in a certain Marxist popular classroom activity in two years is denoted as X, and X obeys the binomial distribution Bn,p. If the number of experiments n of the binomial distribution is large and the probability of success p of each experiment is very small (there are many types of Marxist popular classes, so the participation probability of a certain Marxist popular class is very low), the Poisson distribution can be used as the binomial distribution. The limit is approximate, so for a given total number of participants Y in a Marxist popular classroom, Y obeys a Poisson distribution. As the sample n increases, by the central limit theorem, the Poisson distribution is approximately normal as the sample increases. To prove this progressive point of view, we draw a kernel density map of the frequency of student Marxist popular classroom activities under different capacities, as shown in Figure 6.Figure 6
Kernel density map of frequency distribution of Marxist popular classrooms with different sample sizes.The red in the figure represents the theoretical graph of the normal distribution of the frequency of Marxist popular classrooms, and the sample sizes of blue, green, and orange are 300, 600, and 900, respectively. From the figure, we can see that as the sample size increases, the kernel density function graph of the sample tends to approach the theoretical normal distribution graph.Use a nonparametric test method. Nonparametric tests are often used to compare data from multiple independent samples, by calculating the ranks of the samples rather than the data values to infer whether their distributions are statistically significantly different. Nonparametric test methods mainly include chi-square test, binomial test, and K-S test for pairwise comparison, as well as K-W test, median test, and J-T test for multiple independent samples. Table3 and Figure 7 show the results of K-S test on the activity data of each grade using SPSS23.Table 3
Each node shows the sample mean rank of grade.
Sample 1Sample 2Test statisticsStandard errorStandard test statisticsSalienceAdjusted significance2016-201344.97623.9591.877.060.1812016-2015138.30725.2015.488.000.0002013-2015-93.32820.150-4.632.000.000Figure 7
Pairwise comparison of grades.The above results show that there is no statistically significant difference in the distribution of “learning ability” activities before each grade, and there are significant statistical differences in other types of activities, average academic performance, and graduation destination. This result only shows that there are differences among the three grades as a whole, but whether all three are different, or just two, requires further calculation and also uses the K-S test for pairwise comparison. The above results show that there is no statistically significant difference between the 2016 and 2013 students in the total frequency of Marxist popular classroom activities, but there is a significant difference between the 2015 and these two grades, and their average rank is significantly higher than the other two grades, indicating that student activities for the class of 2015 varied considerably.


## 4.1. Research on the Fusion of AHP and BP Neural Network

In the research field of person-post matching based on BP neural network, researchers found that a single model trained by BP neural network is too objective, and it is difficult to find theoretical support for the distribution of feature weights. At the same time, with the development of interdisciplinary research, researchers want to add weight distribution with theoretical basis of related disciplines to BP neural network. Therefore, with their continuous exploration and discovery, the combination of AHP and BP neural network can meet this assumption.Although this method has improved experimental results, it also has many limitations or defects:(1)
The calculation method of AHP leads to the small capacity of the scheme layer of AHP, which can usually only be used for sorting within 10 elements. Therefore, at the beginning of the research, the competency feature has to be screened, but the result will often eliminate the competency model. The outer factors and only the inner factors are considered, resulting in the limitation of the person-post matching model. At the same time, the amount of test data will be limited during the test(2)
In the combination of AHP and BP neural network, the proportional sum method also has obvious defects. Not only it is difficult to find the theoretical basis for the proportional summation method; it is even more difficult to determine in what proportion the calculation results of AHP and BP neural network are addedBased on the above analysis, it is found that although the traditional combination of AHP and BP neural network has improved the research results, it has not really achieved the researchers’ original vision. Therefore, this study proposes a new combination method—inheritance combination.The inheritance type combination of AHP and BP neural network not only avoids the problem of small AHP capacity but also integrates the artificially set initial weights with the theoretical basis of other disciplines into the calculation of BP neural network. In addition, considering that the normalization process has the problem that the sparsity is destroyed after the data is “squeezed,” the normalization of the processed data may cause the results to be very similar to the normalization results of the original data, which will play a role in “dehierarchical” effect. Therefore, when the inheritance-type combination method is adopted, there is no need to normalize the data.


## 4.2. Comparison and Analysis of Experimental Results

Since this time, the experimental part of this research has all ended, and the experimental results are as shown in Table1 after finishing:Table 1
The accuracy test results of the precise matching model of the political classroom in colleges and universities.
Original groupGroup togetherAHP not groupedAHP group togetherAccuracy50%98.4%99.59%98.4%Compared with the experimental results, it is found that the accuracy of the “original array” is at least 50%, and the normalization of the data has indeed played its advantageous role in the experiment. The “normalized group” not only shortens the training time but also improves its accuracy. to 98.4%.After combining AHP and BP neural network by inheritance, the experimental results are as follows: the accuracy of “AHP is not grouped” is 99.6%, and the accuracy of “AHP is grouped” drops to 98.4%. Combined with the previous “grouped” experimental results, it is found that(1)
in the experiment, AHP can effectively improve the accuracy of the person-post matching model by combining with the BP neural network using the inheritance method(2)
in the experiment of combining AHP and BP neural network, the normalization processing of the hierarchical data makes the accuracy of the human-post matching model fall back to the same value as the training result before the combination. The analysis shows that the reason for this phenomenon is that the normalization of the hierarchical data destroys the sparsity of the hierarchical data, resulting in the phenomenon of de-hierarchical dataTo sum up, this study concluded that “AHP is not grouped” not only integrates the competency hierarchy model related to other disciplines into the model training through AHP, which effectively improves the training speed and accuracy, but also improves the training speed and accuracy. It avoids the destructive sparsity problem of data normalization and then obtains a more scientific and more objective and practical method in the research on accurate matching of political classrooms in colleges and universities, which can effectively help colleges and universities to solve the problem of accurate matching of political classrooms in colleges and universities. The implementation of precise teaching of Ideological and political theory is the key to the implementation of precise ideological and political education in colleges and universities. The three cornerstones of artificial intelligence, algorithm, data, and computing power provide the possibility and feasibility for the precise teaching of Ideological and political theory courses in colleges and universities.


## 4.3. Statistical Analysis of Behavioral Status

In order to test which specific distribution the number of Marxist popular classroom participation obeys, we need to test it. The parametric test and the nonparametric test together constitute the content of statistical inference. The parametric test is what kind of distribution state the known data obeys. For example, theT test and the Z test all assume that the samples come from a normally distributed population, but because we do not know the frequency of students participating in popular Marxist classrooms, it is related to the numerical characteristics of probability, so we can only draw the kernel density map of the total frequency of Marxist popular classroom activities according to the idea of nonparametric statistics, as shown in Figure 2. The kernel density map is a probability graph of actual statistics, so it does not consider the mathematical probability distribution of variables, it is a direct data record, there is no prior estimation, and there is no error, and it can truthfully reflect the real situation of the data. The actual statistic is discrete, so its statistical graph is generally displayed as a histogram, and the kernel density graph is a natural extension of the calculus of the histogram to form a probability density estimation function graph.Figure 2
Kernel density map of the frequency of students participating in Marxist popular classrooms.In Figure2, the abscissa is the total frequency of students’ Marxist popular classroom, and the ordinate is the frequency of occurrence corresponding to these frequencies, that is, the proportion of students. For example, there are 41 students with 20 activities, accounting for 0.043 of the total number of samples, record all the points in the figure, and use the algorithm with the smallest error, such as the least squares method, to get the blue curve in the figure. The red curve is a theoretical normal distribution graph calculated by assuming that the total frequency distribution of students’ Marxist popular classroom activities obeys a normal distribution and calculated with the mean μ and variance σ2 obtained from the statistical data. Intuitively, the graph is close to the normal distribution. In order to further confirm, we test it for normal distribution, and its probability density function graph is shown in Figure 3:Figure 3
Normal distribution diagram.The above figure shows that one of the characteristics of the normal distribution is that the area under the probability density curve with the mean plus or minus one standard deviation covers 68.2% of the median sample size, which is of great value to educational research.The means of testing whether the probability model of data distribution is normally distributed are mainly divided into graphic observation and statistical calculation. Graphical means include Q-Q plots and P-P plots, and statistical means include the Kolmogorov-Smirnov and Shapiro-Wilk tests. SPSS23 software was used to test the normal distribution of the total frequency of students’ Marxist popular classroom activities. The results are shown in Table2 and Figures 4 and 5.Table 2
Normality test.
Kolmogorov-SmirnovShapiro-WilkN total number of second classesStatisticsDegrees of freedomSalienceStatisticsDegrees of freedomSalience.103954.000.932954.000Figure 4
The normal distribution test of the frequency of Marxist popular classroom activities.Figure 5
Histogram of the frequency of Marxist popular classroom activities.The above results show that, from the perspective of calculation and verification, the significance value of the Kolmogorov-Smirnov and Shapiro-Wilk tests is 0.000, which is a very small value, far smaller than the general statistical confidence interval of 0.05, which is the total frequency distribution of this research case. It does not obey a normal distribution.From the Q-Q graph and histogram, the measured value and the normal value curve have a certain degree of agreement. Therefore, we speculate that when the sample sizen increases infinitely, the limiting distribution is a normal distribution, that is, asymptotic normal distribution. For the asymptotic normality of the data, we have the following interpretation. For any student, the number of times he participated in a certain Marxist popular classroom activity in two years is denoted as X, and X obeys the binomial distribution Bn,p. If the number of experiments n of the binomial distribution is large and the probability of success p of each experiment is very small (there are many types of Marxist popular classes, so the participation probability of a certain Marxist popular class is very low), the Poisson distribution can be used as the binomial distribution. The limit is approximate, so for a given total number of participants Y in a Marxist popular classroom, Y obeys a Poisson distribution. As the sample n increases, by the central limit theorem, the Poisson distribution is approximately normal as the sample increases. To prove this progressive point of view, we draw a kernel density map of the frequency of student Marxist popular classroom activities under different capacities, as shown in Figure 6.Figure 6
Kernel density map of frequency distribution of Marxist popular classrooms with different sample sizes.The red in the figure represents the theoretical graph of the normal distribution of the frequency of Marxist popular classrooms, and the sample sizes of blue, green, and orange are 300, 600, and 900, respectively. From the figure, we can see that as the sample size increases, the kernel density function graph of the sample tends to approach the theoretical normal distribution graph.Use a nonparametric test method. Nonparametric tests are often used to compare data from multiple independent samples, by calculating the ranks of the samples rather than the data values to infer whether their distributions are statistically significantly different. Nonparametric test methods mainly include chi-square test, binomial test, and K-S test for pairwise comparison, as well as K-W test, median test, and J-T test for multiple independent samples. Table3 and Figure 7 show the results of K-S test on the activity data of each grade using SPSS23.Table 3
Each node shows the sample mean rank of grade.
Sample 1Sample 2Test statisticsStandard errorStandard test statisticsSalienceAdjusted significance2016-201344.97623.9591.877.060.1812016-2015138.30725.2015.488.000.0002013-2015-93.32820.150-4.632.000.000Figure 7
Pairwise comparison of grades.The above results show that there is no statistically significant difference in the distribution of “learning ability” activities before each grade, and there are significant statistical differences in other types of activities, average academic performance, and graduation destination. This result only shows that there are differences among the three grades as a whole, but whether all three are different, or just two, requires further calculation and also uses the K-S test for pairwise comparison. The above results show that there is no statistically significant difference between the 2016 and 2013 students in the total frequency of Marxist popular classroom activities, but there is a significant difference between the 2015 and these two grades, and their average rank is significantly higher than the other two grades, indicating that student activities for the class of 2015 varied considerably.


## 5. Conclusion

In the course of the popularization of ideological and political Marxism classroom activities, various current advanced information technology means, especially big data analysis, should be used reasonably, which can enhance the scientific and intelligent level of this work. Although big data can bring convenience to the work of colleges and universities, it will also face new problems, which may lead to the emergence of ethical problems. In order to control these unhealthy situations or even avoid their development completely, adjustments must be made in terms of systems, management, thinking, and talents, so as to improve the quality of ideological and political work in Marxist popular classrooms. It is necessary to systematically and holistically design and adjust the Marxist popular classroom education activities to form a perfect guarantee mechanism. In terms of cultivating talents, the importance of the Marxist popular classroom cannot be ignored, especially in the process of the full implementation of ideological and political education, the Marxist popular classroom can have a more silent effect, and it should be systematically designed and planned. The development of Marxist popular classroom activities must have a sound organizational structure, as well as a benign operating mechanism, and it is inseparable from reasonable organizational management. Only by improving the standardization of management can various activities in the Marxist popular classroom develop in an orderly manner. Every college student has different hobbies. They all hope to have the opportunity to show their talents. Association activities can attract them infinitely. It is not difficult to encourage college students to participate in association activities. Many of these experiences can be applied to the popularization of Marxism. When designing the management system for the Marxist popular classroom, it must be scientific and reasonable, but it must also reflect freedom and tolerance. It must be orderly and standardized, but also flexible and competitive, so that the Marxist popular classroom can lead students to gradually possess the “three-self” ability to avoid excessive interference in this activity by bureaucratic administration.



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*Source: 5908754-2022-08-27.xml*