# A Quantitative Enhancement Mechanism of University Students’ Employability and Entrepreneurship Based on Deep Learning in the Context of the Digital Era

Authors: Xiangmin Meng; Guoyan Ren; Wenjun Huang
Journal: Scientific Programming
 (2021)

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## Abstract

This paper adopts a deep learning approach to analyze and study the mechanism of quantitative enhancement of college students’ employment and entrepreneurial abilities in the context of the digital era. The deep learning connotation is predetermined as five abilities, which are metacognitive ability, active communication and cooperation ability, deep processing ability, creative practice ability, and learning empathy experience, and, based on this, the deep learning questionnaire is designed, and it is reclassified by exploratory factor analysis to reduce the dimensionality, and the specific indicators and scientific connotation dimensions of the deep learning questionnaire are determined; and, through the deep learning of each dimension, the problems of deep learning of college students are examined and in-depth analysis is conducted, and the inner relationship and correlation among the dimensions of deep learning of college students are derived through correlation analysis. The success of innovation and entrepreneurship depends on the innovation and entrepreneurial ability of college students, and the formation of the ability influenced various factors. Therefore, not only is studying the influencing factors of college students’ innovation and entrepreneurship ability in line with the requirements of the times and social development, but also it can solve real problems. This thesis adopts a combination of two methods, qualitative research and quantitative research, to study the influencing factors of college students’ innovation and entrepreneurship ability and tries to ensure the scientificity, accuracy, and comprehensiveness of the conclusion. In this paper, we analyzed the requirements of the employment prediction system for graduating secondary school students, carried out the software framework and database design of the employment analysis and prediction system for secondary school students, and designed the system modularly based on the analysis results. By applying the proposed deep feedforward neural network prediction model to the prediction system, a software system applicable to the employment prediction and guidance of secondary school students is implemented.


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## Body

## 1. Introduction

In today’s global economic system which is constantly developing and changing, whoever has the most innovative technology and the most innovative talents will have the first opportunity, and countries all over the world have launched the innovation-driven development strategy. Therefore, it is crucial to study the factors affecting the success of college students’ entrepreneurship, and the ability of college students is the key that directly determines the success of their entrepreneurship, so it is crucial to study the ability of college students’ entrepreneurship. As the future force of social development, the improvement of the entrepreneurial ability of college students is related to the implementation of China’s innovative country construction plan and the speed of implementation [1]. Therefore, it is an effective way to solve the difficulties of entrepreneurship, the low success rate of entrepreneurship, and the pressure of social employment for college students by studying the factors influencing the entrepreneurial ability of college students and exploring the strategies to improve the it in three levels [2]. Therefore, the dataset needs to be segmented, and the model parameters are continuously adjusted through the verification set to allow the model to converge in a better direction. Not only can the improvement of college students’ entrepreneurial ability improve their entrepreneurial success rate, but also the research on college students’ ability can help to promote the cultivation of entrepreneurial talents and play a good role of promoting employment with entrepreneurship to drive social and economic development, enhance the country’s economic strength, accelerate the construction of an innovative country, and improve the competitiveness for China in the international economic competition.Using the method of qualitative research, through the integration, analysis, and refinement of a large amount of interview data, this paper constructs a model of the factors influencing college students’ entrepreneurial ability and uses the model to discuss the factors influencing college students’ entrepreneurial ability. To ensure the scientific and reasonable theoretical framework, based on the theoretical model of college students’ entrepreneurial ability constructed, quantitative research on college students’ entrepreneurial ability was conducted using statistical software [3]. In the research process, we make full use of the combination of qualitative and quantitative research methods, especially through in-depth interviews with college student entrepreneurs, to summarize and analyze the factors influencing the entrepreneurial ability of college student entrepreneurs and adopt questionnaire research to supplement the model of the previous qualitative research. Based on the survey data originating from the college student group, it truly reflects the influencing factors of college students’ entrepreneurial ability, enriches the research theory of college students’ entrepreneurial ability, and proposes entrepreneurial ability improvement strategies for the influencing factors, which provides a theoretical basis for the improvement of college students’ entrepreneurial ability [4]. The employment situation of graduates from secondary colleges and universities is studied from various angles, and the problems and shortcomings of graduates in the employment process are summarized and analyzed through the analysis and tracking of the employment situation, to reflect on whether the current teaching methods and teaching priorities of secondary colleges and universities can provide students with good employment guidance and employment assistance and whether the current teaching model can cultivate excellent graduates who meet the needs of the country and society, to come up with conclusions that are conducive to adjusting the direction of career guidance and improving the quality of teaching in secondary institutions [5]. From this grid form, we can clearly see the result that these parameters can be combined. This is of far-reaching significance for the country, schools, and individuals.The main feature of an innovative country is to realize the transformation of human resources into human capital, especially to make outstanding talents become the backbone to drive the efficient development of the economy and optimization and upgrading of industrial structure. The great demand for innovative talents in national social development has further promoted the development of innovation and entrepreneurship education in colleges and universities to cultivate and improve the innovation and entrepreneurship ability of college students, making it an important aspect of the construction of double-class universities. From the background of innovation and entrepreneurship, promoting innovation and entrepreneurship among college students is an important measure to respond to the development of new technologies and industries and to build an innovative country; from the background of the reform of entrepreneurship education in colleges and universities, cultivating entrepreneurial human capital among college students is an important path to deepen the reform of entrepreneurship education, implement the goal of quality education cultivation, realize comprehensive development of college students, and enhance social adaptability; from the background of improving national competitiveness, promoting innovation and entrepreneurship among college students is an important path to deepen the reform of entrepreneurship education, implement the goal of quality education cultivation, realize comprehensive development of college students, and enhance social adaptability. From the background of improving national competitiveness, promoting college students’ innovation and entrepreneurship is an important means to give full play to the advantages of human resources and an important strategic plan to build a strong human resources country. In such a background, it is urgent to solve the problem of employment difficulties of graduates, and it has already aroused considerable concern in society. Through the analysis and refining of the initial sampling, sample selection and data mining are performed to carry out research work in a state of “theory saturation.” If college students are unable to find suitable jobs after graduation and thus become unemployed, they will not be able to give full play to their strengths in various fields and add to the construction of socialist modernization. In this way, on the one hand, this will aggravate the conflicts in society, and, on the other hand, it will be a great waste of our human resources. According to incomplete statistics on the employment outlook of graduates, about 90% of school students are worried about their future employment prospects. A very important reason for this phenomenon is that college students do not receive good guidance during their school years, do not have a very clear career plan, and spend all day getting by, which eventually leads to employment failure. Therefore, early warning and guidance for school students are a proven solution to this phenomenon.


## 2. Current Status of Research

Neural network algorithms have made breakthroughs. A group of scientists have proposed the famous error backpropagation (BP) algorithm. The backpropagation algorithm is based on the traditional neural network algorithm, which continuously adjusts the weights and thresholds between neurons to correct the model error during the backpropagation process, significantly reducing the complexity of the model computation process [6]. The final output error of the model is reduced below an allowable threshold or terminated when the set number of training times is reached. The BP algorithm solves the nonlinear classification problem in neural network models, making a significant advance in the difficulty and scope of its use, and people once again start to focus on artificial neural networks [7]. In the research, firstly, through the unsupervised learning method, the model was trained in multiple levels, and, on this basis, the supervised backpropagation algorithm was used for parameter tuning and model correction, which successfully solved the “gradient disappearance” problem, which marked the formal introduction of the concept of “deep learning” [8]. Once deep learning was proposed, it caused a great sensation in the academic world, and world-renowned universities, represented by Stanford University, began to spend huge amounts of money and workforce on research in deep-learning-related fields. Deep learning methods have also been favored by industry, with a representative group using the deep learning model AlexNet to win the ImageNet image recognition competition. The model also took advantage of the GPU’s strengths in floating-point computing, which greatly increased the speed of computing [9]. In the same year, renowned Stanford University machine learning professor Enda Wu developed a deep neural net model in conjunction with the world’s leading computer experts [10]. The model extended the layers of traditional neural networks and achieved great success in the field of image recognition. Deep learning algorithms have once again attracted the attention of scholars and industry worldwide due to their superior performance.The practice in student career guidance first began in Europe and the United States. In the United States, colleges and universities attach great importance to the employment of students, and almost all colleges and secondary schools in the United States have their career guidance organizations for all students [9]. In the UK, higher education institutions will start career guidance work for students from the moment they first enter the school and set up special career guidance files for all students in the school, so that they can carry out guidance work according to the individual students’ needs so that students can be better employed [11]. According to the search results, we can see that the current career guidance in China’s education is dominated by college students and higher education institutions, while information related to career guidance for students in secondary schools does not appear in the search buzzwords [12]. In addition, the search results counted subject distribution, and the distribution of the top ten subjects was obtained. From the results, higher education occupies the vast majority, followed by vocational education. This means that career guidance is also gradually gaining attention in vocational education in China, and the employment situation of students in secondary schools is getting the attention of society [13].The purpose of this study is to predict the employment situation of secondary school graduates and provide referenceable solutions to the problems encountered by institutions in guiding students’ employment. In recent years, some scholars in China have conducted relevant research on the prediction problem from different perspectives and using different algorithms. The research and application of the graduate employment prediction model using machine learning have been conducted, and, based on the research and analysis results, the hybrid feature selection algorithm with mutual information and weights (HMIGW) and XGBoost feature selection algorithm have been combined to provide an effective solution for student employment prediction, and the algorithm has been further optimized and improved based on the implementation feedback from relevant departments. The semisupervised self-training method was improved and applied to the applied research of student employment prediction in colleges and universities, combining the analysis of the prediction results of the data to be graduated students and the actual situation of students to propose guidance countermeasures, and its model has obvious advantages. The classical decision tree algorithm is used to construct a decision tree prediction model of the factors influencing students’ employment, such as “whether to be employed” and “employment area,” and the two models are applied to the students’ employment guidance work. The natural nearest neighbor classification algorithm is used to predict the employability of college students. The model is based on the employment information input of graduates and is used to build a salary prediction model for employed college students.


## 3. Quantitative Analysis of Entrepreneurship Based on Deep Learning in the Context of Digital Era

### 3.1. Deep-Learning-Based Algorithm Design in the Context of the Digital Age

Artificial neural networks evolved from the original neuron model; inspired by biology, the learning system in the biological brain is a vast network of countless neurons, based on a topology that incorporates the basic principles of neural networks in biology, connecting many neurons to one another to process the input data [14]. In an artificial neural network, each neuron represents a specific activation function and every connection between two neurons represents a weight. In artificial neural networks, the weights and activation functions differ, and the output will be different. In order to respond to the requirements of national development for technological innovation, we need to cultivate students’ corresponding innovation and entrepreneurship ability. For example, the classical algorithm in neural networks-backpropagation algorithm, based on the input layer, hidden layer, and output layer, constructs artificial neural networks, in the process of data transmission, constantly using gradient descent and adjusting the parameters, so that the error between the actual output and the desired output achieves minimum convergence and the best fit, greatly improving the robustness of data processing, and the neuron structure is shown in Figure 1.Figure 1
Deep neural network structure.Deep neural networks are predecessors of artificial neural networks, but, unlike artificial neural networks, they have more hidden layers. In deep neural networks, the model building has a strong structural feature. The interface of the network model is the input layer, which is also the entrance of the data into the model, and after the data passes through the input layer, it will enter the hidden layer, and the hidden layer is not only one layer after the data is transformed and calculated by multiple hidden layers; the result obtained is passed into the output layer and the final information is obtained. Deep feedforward neural network is a typical deep learning model whose goal is to fit some function to transform the input into some predicted output and learn the network parameters at the same time so that the model gets the optimal approximation of the function. Since there is no feedback connection to the model itself during the process from the input to the output, this model is called a feedforward model [15]. Deep feedforward networks are also composed of multiple functions compounded together, and the model is often associated with a directed acyclic graph, where the graph describes how the functions compounded and the full length of the chain is defined as the depth of the model. In practice, problems are often linearly indistinguishable and require nonlinear transformations to remap the distribution of the data, and the deep neural network itself goes through multiple levels of linear changes, with each change superimposing a nonlinear activation function, distinguishing it from a single layer of linear functions, where the multilayer network structure enables the construction of more accurate models, resulting in more powerful learning and fitting capabilities.After building a deep feedforward neural network model, model evaluation is a key part, and the loss function defines the evaluation metric of the model when backpropagating in the network. As the name suggests, the loss function measures the difference between the predicted value of the output of the neural network and the actual value of a way to measure how good the network is, as well as by backpropagating the error to gradually improve the performance of the network on the prediction after data processing. The difficulty of optimizing the loss function varies with the parameters, the final model parameters obtained vary, and the appropriate loss parameters need to be selected for the specific problem. Common loss functions include least-squares loss function, cross-entropy loss function, and Smooth L1 loss function. In supervised learning, the loss functions we commonly use are mainly the least-squares loss function and the cross-entropy loss function.(1)Ly,fx,θ14y+fx,θ2.The multiclassification cross-entropy loss function is an extension of the binary classification, with the following equation:(2)Hyi,yj=∑i,j=1Qyilnyj.After determining the loss function, the model has all the functional structures to perform data processing and the next step is to determine the optimization algorithm for the model when performing backpropagation. The optimization algorithms for deep learning are stochastic gradient descent methods. In the gradient descent method, again, there are different forms of operations. The batch gradient descent method iterates through the entire sample of the network model to update the weights of the network, which greatly increases the number of operations on the network data and reduces the speed of backpropagation of network errors. The more professional and in-depth related abilities that need to be possessed in the later development period and maturity period are not the focus of training at this stage. The innovative and entrepreneurial abilities that should be possessed in the awakening stage should be generally oriented to all college students, with emphasis on innovation spirit, learning ability, entrepreneurial personality, and so forth; and the innovative and entrepreneurial abilities that should be possessed in the initial stage are mainly aimed at the transformation of scientific and technological achievements and practicality. In contrast, the stochastic gradient descent method will only iterate through one of the samples of the network model input samples to update the weights, which will increase the speed of the network to reduce the amount of computation, but because the iterations use too little data, this often leads to a decrease in the accuracy of the network model, which makes the result be a local optimal solution, but it cannot get the global optimal solution.Dropout is a random strategy in the training process of a deep model, that is, dropping a neuron node in a neural network at random with a certain probability. Since the dropout method acts on a different small batch of training data each time, each time a random part of neurons is dropped can be seen as a retraining process of the neural network, resulting in a completely different network structure [16]. Dropout is compared to the Bagging method, which is traditionally used for large-scale deep neural network training methods due to its involvement of the overall model and data; when the size of the network is large, this integration method not only takes a lot of computational time but also consumes a lot of data storage space. Therefore, the Bagging method does not have a great advantage when training small batches of data. Dropout, as a lightweight alternative to Bagging, can achieve the good properties of the Bagging method on small batches of data, that is, to achieve exponential numbers of neural networks for training and evaluation.(3)Zk=expxk∑i=1kexpxk.To apply deep neural networks for employment prediction, the computational process based on deep neural networks is given in Figure2. The main processes are as follows: first, the dataset is preprocessed and all types of features are transformed into numerical features using one-hot coding; second, the dataset is divided into a training set and a test set in the ratio of 7 : 3, and the deep neural network is trained using the feature data from the training set and the training results are verified using the test set to find the best combination of parameters using a grid search method.Figure 2
Computational flow based on deep feedforward neural network.The definition of evaluation metrics is important for the evaluation of a network model and is an important factor in validating the quality of the network model; in this paper, accuracy, recall, and F1 value are selected as the evaluation metrics of the network model. For the categories, the formula is as follows:(4)pc=TPTP-FP,Rc=TPTP+FN-1,Fc=1−β2−pc+Rcβ2−Rc.In deep learning, the data needs to be sliced and normalized for training and testing. The dataset is divided into the training set, validation set, and test set; the training set is used to train the model, the validation set is used to observe the model performance, and the test set is used to verify the model effectiveness. The model obtained from the training set alone does not prove that the prediction model is effective and may reduce the usefulness of the model due to overfitting, so the dataset needs to be sliced and the model parameters are continuously adjusted through the validation set so that the model converges towards a better direction. First, the dataset needs to be split into two parts: the training set and the test set. Nowadays, there are two popular methods of data partitioning: one is that 80% of the data is used for training and 20% of the data is used for testing, and the other is that 70% of the data is used for training and 30% of the data is used for testing. To better test the generalization ability of the model, the second method is chosen. 30% of the data is divided into the test set, which is not trained for model parameters and is only used as the final test evaluation of the model. Meanwhile, for the first 70% of the data, 10% is randomly selected as the validation set, which will act as a test evaluation within the training for each training of the model, while the remaining 60% or so of the data is the data that is trained for the parameters. They are 0.443 and 0.387, respectively. Among the three-level indicators, initiative spirit, reform willingness, and abiding by the contract have larger weights, indicating that they are more important to the spirit of innovation; in the evaluation of the various elements of innovation and entrepreneurship ability, the ability to transform results has the highest weight.(5)Kx1,x2=expγx1−x22.The method of lattice search belongs to the classical exhaustive search method from the mathematical essence. In this paper, all the possible values of the parameters of the network model are combined in a specific permutation, and after they are combined, the result of the permutation is presented in the form of a grid, from which we can see the results that can be combined into these parameters [17]. Then all the parameters in the formed grid can be combined in a way to feed the network model, and then the whole network model will be trained, the performance of the network model will be continuously evaluated, and the performance parameters after each evaluation will be compared so that the best combination of parameters can be continuously filtered.


### 3.2. Quantitative Design of University Students' Employability and Entrepreneurship

The theory and practice of statistics tell us that the connotation of everything comes from its essential properties, and the revelation of its essential properties comes from the data and information mining that portray its appearance [18]. Therefore, in the face of such a complex research topic as the qualitative study of the factors influencing college students’ entrepreneurial ability, the key to achieving the purpose of the study is the way to collect data and to reprocess the data on this basis to obtain the most reliable information. Considering the limitations and timeliness of secondary data, according to the principle of “the best performance to price ratio” and focusing on the qualitative indicators that can best describe the factors influencing college students’ entrepreneurial ability, we adopted a combination of theoretical sampling and interviewing and carried out sample selection and data mining through the analysis and refinement of the initial sample. The study was carried out in a state of “theoretical saturation” through sample selection and data mining. The optimal sample size is the smallest amount of data saturated with the highest level of content required to achieve the research objectives [14]. According to the requirements and operation procedures of in-depth interviews, to ensure the representativeness of the data, we first determined the sampling frame of regions, taking into account the concentration of college students’ entrepreneurial bases and the number of entrepreneurs, through existing records and fieldwork, and then further determined the sampling frame of survey members, requiring that those who can be used as sample members must have at least one successful experience of entrepreneurship during their school years or within two years of graduation.In this way, they can provide more convincing information, such as the initial intention of starting a business, the confusion of starting a business, what are the main factors affecting the business, what abilities are needed in the process of starting a business, and what kind of environment is more favorable for starting a business. According to the number of entrepreneurs and the current situation of entrepreneurship, stratified random sampling is adopted to draw a sample of 30 people from each area in the sampling area box, respectively; then interviews are conducted, the order of interviews is determined by drawing lots, the interviews start from number one, and if the interviewer can no longer provide new information they will be eliminated; to define the concept of the innovative entrepreneurial ability of college students, we cannot ignore the special characteristics of the college student group, so that its concept is different from the general concept of innovative entrepreneurial ability. The abovementioned method will be an effective way to solve the difficulties of college students in the aspects of starting a business, reducing success rate of starting a business and the pressure of social employment. To define the concept of innovation and entrepreneurship among university students, it is necessary to differentiate the concept from the general concept of innovation and entrepreneurship and to narrow down the scope and make it more specific and relevant. Unlike the entrepreneurs and start-ups studied in the past, the entrepreneurship of university students advocated by the state is not just as simple as “setting up a stall” or “doing a small business.”It should be an innovative and valuable venture. Therefore, based on the existing literature research and the characteristics of our society and college students, we define the concept of college students’ innovative entrepreneurial ability as follows: the ability of college students to create and improve new things and transform them into economic, social, and cultural values that are beneficial to personal or social development by using their scientific and cultural knowledge and surrounding resources, which is mostly reflected in the innovative entrepreneurial primary stage, as shown in Figure3.Figure 3
Hypothetical model diagram.At the critical period of accelerating the construction of an innovative country, great importance is attached to the cultivation of innovative talents and university students, as highly sophisticated talents who master scientific and cultural knowledge and advanced technology are the main force to achieve this grand strategic goal. Therefore, in the context of “mass entrepreneurship and innovation,” the innovation and entrepreneurship ability of college students should actively respond to the theme of the times and reflect the characteristics of the new era. Therefore, the modernity of the innovation and entrepreneurship ability of college students refers to the fact that college students closely combine their ideal beliefs and life pursuits with national development and social progress, respond to the development requirements of national science and technology innovation, knowledge innovation, and system innovation, as well as strategic opportunities, focus on new technologies, new business modes, and new models, and take the initiative to accumulate and improve or cultivate the corresponding innovation and entrepreneurship ability with external support. This model is used to discuss the influencing factors of college students’ entrepreneurial ability. To ensure that the theoretical framework is scientific and reasonable, based on constructing a theoretical model of college students’ entrepreneurial ability, statistical software is used to conduct quantitative research on college students’ entrepreneurial ability. The new round of business innovation and technological change is reshaping the global economic landscape, and countries all over the world are emphasizing the importance of innovation, but, due to the different social systems, different degrees of development, and different development processes in each country, the requirements for the innovation and entrepreneurship capabilities of college students also vary, and each country should develop corresponding specific strategies in line with national realities and development needs.The innovation and entrepreneurship capabilities that need to be possessed or focused on by innovation and entrepreneurship subjects are different for each stage. Unlike other innovative entrepreneurial groups in society, such as entrepreneurs and migrant workers, the time frame for this particular group of university students is mainly limited to the period during which they attend university and within two years of graduation, and the corresponding entrepreneurial stage is the awakening of consciousness and the start-up phase, which focuses more on the innovative spirit, learning ability, and the knowledge and skills required for the start-up phase [19]. The more specialized and in-depth competencies required in the later development and maturity stages are not the focus of this stage. The innovation and entrepreneurial ability that should be possessed in the awakening stage should be generally oriented to all college students, focusing on the innovation spirit, learning ability, and entrepreneurial personality, while the innovation and entrepreneurial ability that should be possessed in the start-up stage mainly targets a small group of people who transform scientific and technological achievements and start a business, focusing on enterprise management ability and so forth, as shown in Figure 4.Figure 4
Descriptive statistics of organizational mechanisms.The data obtained through survey research in quantitative research is the basis for scientific decision-making, and the selection of appropriate data analysis methods and data analysis software is an effective means to ensure scientific decision-making. According to the research needs and research questions, this paper will use descriptive statistical analysis, factor analysis, hierarchical analysis (AHP), principal component analysis (PCA), and full weight method to determine the weights of innovation and entrepreneurship education ecosystem composition indicators of research universities. Among them, the main methods used in the weight analysis were hierarchical analysis (AHP), principal component analysis (PCA), and the full weight method. In the specific data processing, statistical analysis software programs such as SPSS and AMOS were used as the main analytical tools for data analysis. The specific statistical analysis steps are as follows: Firstly, the research group distributed the questionnaire of “the composition index of innovation and entrepreneurship education ecosystem in research universities” to each university, the subjects were teachers and students, and the survey data were recorded into electronic files. Aiming at the influencing factors, a strategy to improve entrepreneurial ability was proposed, which provided a theoretical basis for the improvement of college students’ entrepreneurial ability. Secondly, according to the questionnaire responses, incomplete responses, regular responses, short responses, and self-contradictory responses were eliminated, and valid questionnaires were obtained. Again, using SPSS statistical software, the obtained data were processed, and descriptive statistical analysis, exploratory factor analysis, and validation factor analysis were conducted on the questionnaire to determine that the questionnaire had good reliability. Finally, the combination of hierarchical analysis, principal component analysis, and full weight method was used to determine the weights of the composition indicators of the innovation and entrepreneurship education ecosystem of research universities to help managers better determine the focus of their work.


## 3.1. Deep-Learning-Based Algorithm Design in the Context of the Digital Age

Artificial neural networks evolved from the original neuron model; inspired by biology, the learning system in the biological brain is a vast network of countless neurons, based on a topology that incorporates the basic principles of neural networks in biology, connecting many neurons to one another to process the input data [14]. In an artificial neural network, each neuron represents a specific activation function and every connection between two neurons represents a weight. In artificial neural networks, the weights and activation functions differ, and the output will be different. In order to respond to the requirements of national development for technological innovation, we need to cultivate students’ corresponding innovation and entrepreneurship ability. For example, the classical algorithm in neural networks-backpropagation algorithm, based on the input layer, hidden layer, and output layer, constructs artificial neural networks, in the process of data transmission, constantly using gradient descent and adjusting the parameters, so that the error between the actual output and the desired output achieves minimum convergence and the best fit, greatly improving the robustness of data processing, and the neuron structure is shown in Figure 1.Figure 1
Deep neural network structure.Deep neural networks are predecessors of artificial neural networks, but, unlike artificial neural networks, they have more hidden layers. In deep neural networks, the model building has a strong structural feature. The interface of the network model is the input layer, which is also the entrance of the data into the model, and after the data passes through the input layer, it will enter the hidden layer, and the hidden layer is not only one layer after the data is transformed and calculated by multiple hidden layers; the result obtained is passed into the output layer and the final information is obtained. Deep feedforward neural network is a typical deep learning model whose goal is to fit some function to transform the input into some predicted output and learn the network parameters at the same time so that the model gets the optimal approximation of the function. Since there is no feedback connection to the model itself during the process from the input to the output, this model is called a feedforward model [15]. Deep feedforward networks are also composed of multiple functions compounded together, and the model is often associated with a directed acyclic graph, where the graph describes how the functions compounded and the full length of the chain is defined as the depth of the model. In practice, problems are often linearly indistinguishable and require nonlinear transformations to remap the distribution of the data, and the deep neural network itself goes through multiple levels of linear changes, with each change superimposing a nonlinear activation function, distinguishing it from a single layer of linear functions, where the multilayer network structure enables the construction of more accurate models, resulting in more powerful learning and fitting capabilities.After building a deep feedforward neural network model, model evaluation is a key part, and the loss function defines the evaluation metric of the model when backpropagating in the network. As the name suggests, the loss function measures the difference between the predicted value of the output of the neural network and the actual value of a way to measure how good the network is, as well as by backpropagating the error to gradually improve the performance of the network on the prediction after data processing. The difficulty of optimizing the loss function varies with the parameters, the final model parameters obtained vary, and the appropriate loss parameters need to be selected for the specific problem. Common loss functions include least-squares loss function, cross-entropy loss function, and Smooth L1 loss function. In supervised learning, the loss functions we commonly use are mainly the least-squares loss function and the cross-entropy loss function.(1)Ly,fx,θ14y+fx,θ2.The multiclassification cross-entropy loss function is an extension of the binary classification, with the following equation:(2)Hyi,yj=∑i,j=1Qyilnyj.After determining the loss function, the model has all the functional structures to perform data processing and the next step is to determine the optimization algorithm for the model when performing backpropagation. The optimization algorithms for deep learning are stochastic gradient descent methods. In the gradient descent method, again, there are different forms of operations. The batch gradient descent method iterates through the entire sample of the network model to update the weights of the network, which greatly increases the number of operations on the network data and reduces the speed of backpropagation of network errors. The more professional and in-depth related abilities that need to be possessed in the later development period and maturity period are not the focus of training at this stage. The innovative and entrepreneurial abilities that should be possessed in the awakening stage should be generally oriented to all college students, with emphasis on innovation spirit, learning ability, entrepreneurial personality, and so forth; and the innovative and entrepreneurial abilities that should be possessed in the initial stage are mainly aimed at the transformation of scientific and technological achievements and practicality. In contrast, the stochastic gradient descent method will only iterate through one of the samples of the network model input samples to update the weights, which will increase the speed of the network to reduce the amount of computation, but because the iterations use too little data, this often leads to a decrease in the accuracy of the network model, which makes the result be a local optimal solution, but it cannot get the global optimal solution.Dropout is a random strategy in the training process of a deep model, that is, dropping a neuron node in a neural network at random with a certain probability. Since the dropout method acts on a different small batch of training data each time, each time a random part of neurons is dropped can be seen as a retraining process of the neural network, resulting in a completely different network structure [16]. Dropout is compared to the Bagging method, which is traditionally used for large-scale deep neural network training methods due to its involvement of the overall model and data; when the size of the network is large, this integration method not only takes a lot of computational time but also consumes a lot of data storage space. Therefore, the Bagging method does not have a great advantage when training small batches of data. Dropout, as a lightweight alternative to Bagging, can achieve the good properties of the Bagging method on small batches of data, that is, to achieve exponential numbers of neural networks for training and evaluation.(3)Zk=expxk∑i=1kexpxk.To apply deep neural networks for employment prediction, the computational process based on deep neural networks is given in Figure2. The main processes are as follows: first, the dataset is preprocessed and all types of features are transformed into numerical features using one-hot coding; second, the dataset is divided into a training set and a test set in the ratio of 7 : 3, and the deep neural network is trained using the feature data from the training set and the training results are verified using the test set to find the best combination of parameters using a grid search method.Figure 2
Computational flow based on deep feedforward neural network.The definition of evaluation metrics is important for the evaluation of a network model and is an important factor in validating the quality of the network model; in this paper, accuracy, recall, and F1 value are selected as the evaluation metrics of the network model. For the categories, the formula is as follows:(4)pc=TPTP-FP,Rc=TPTP+FN-1,Fc=1−β2−pc+Rcβ2−Rc.In deep learning, the data needs to be sliced and normalized for training and testing. The dataset is divided into the training set, validation set, and test set; the training set is used to train the model, the validation set is used to observe the model performance, and the test set is used to verify the model effectiveness. The model obtained from the training set alone does not prove that the prediction model is effective and may reduce the usefulness of the model due to overfitting, so the dataset needs to be sliced and the model parameters are continuously adjusted through the validation set so that the model converges towards a better direction. First, the dataset needs to be split into two parts: the training set and the test set. Nowadays, there are two popular methods of data partitioning: one is that 80% of the data is used for training and 20% of the data is used for testing, and the other is that 70% of the data is used for training and 30% of the data is used for testing. To better test the generalization ability of the model, the second method is chosen. 30% of the data is divided into the test set, which is not trained for model parameters and is only used as the final test evaluation of the model. Meanwhile, for the first 70% of the data, 10% is randomly selected as the validation set, which will act as a test evaluation within the training for each training of the model, while the remaining 60% or so of the data is the data that is trained for the parameters. They are 0.443 and 0.387, respectively. Among the three-level indicators, initiative spirit, reform willingness, and abiding by the contract have larger weights, indicating that they are more important to the spirit of innovation; in the evaluation of the various elements of innovation and entrepreneurship ability, the ability to transform results has the highest weight.(5)Kx1,x2=expγx1−x22.The method of lattice search belongs to the classical exhaustive search method from the mathematical essence. In this paper, all the possible values of the parameters of the network model are combined in a specific permutation, and after they are combined, the result of the permutation is presented in the form of a grid, from which we can see the results that can be combined into these parameters [17]. Then all the parameters in the formed grid can be combined in a way to feed the network model, and then the whole network model will be trained, the performance of the network model will be continuously evaluated, and the performance parameters after each evaluation will be compared so that the best combination of parameters can be continuously filtered.


## 3.2. Quantitative Design of University Students' Employability and Entrepreneurship

The theory and practice of statistics tell us that the connotation of everything comes from its essential properties, and the revelation of its essential properties comes from the data and information mining that portray its appearance [18]. Therefore, in the face of such a complex research topic as the qualitative study of the factors influencing college students’ entrepreneurial ability, the key to achieving the purpose of the study is the way to collect data and to reprocess the data on this basis to obtain the most reliable information. Considering the limitations and timeliness of secondary data, according to the principle of “the best performance to price ratio” and focusing on the qualitative indicators that can best describe the factors influencing college students’ entrepreneurial ability, we adopted a combination of theoretical sampling and interviewing and carried out sample selection and data mining through the analysis and refinement of the initial sample. The study was carried out in a state of “theoretical saturation” through sample selection and data mining. The optimal sample size is the smallest amount of data saturated with the highest level of content required to achieve the research objectives [14]. According to the requirements and operation procedures of in-depth interviews, to ensure the representativeness of the data, we first determined the sampling frame of regions, taking into account the concentration of college students’ entrepreneurial bases and the number of entrepreneurs, through existing records and fieldwork, and then further determined the sampling frame of survey members, requiring that those who can be used as sample members must have at least one successful experience of entrepreneurship during their school years or within two years of graduation.In this way, they can provide more convincing information, such as the initial intention of starting a business, the confusion of starting a business, what are the main factors affecting the business, what abilities are needed in the process of starting a business, and what kind of environment is more favorable for starting a business. According to the number of entrepreneurs and the current situation of entrepreneurship, stratified random sampling is adopted to draw a sample of 30 people from each area in the sampling area box, respectively; then interviews are conducted, the order of interviews is determined by drawing lots, the interviews start from number one, and if the interviewer can no longer provide new information they will be eliminated; to define the concept of the innovative entrepreneurial ability of college students, we cannot ignore the special characteristics of the college student group, so that its concept is different from the general concept of innovative entrepreneurial ability. The abovementioned method will be an effective way to solve the difficulties of college students in the aspects of starting a business, reducing success rate of starting a business and the pressure of social employment. To define the concept of innovation and entrepreneurship among university students, it is necessary to differentiate the concept from the general concept of innovation and entrepreneurship and to narrow down the scope and make it more specific and relevant. Unlike the entrepreneurs and start-ups studied in the past, the entrepreneurship of university students advocated by the state is not just as simple as “setting up a stall” or “doing a small business.”It should be an innovative and valuable venture. Therefore, based on the existing literature research and the characteristics of our society and college students, we define the concept of college students’ innovative entrepreneurial ability as follows: the ability of college students to create and improve new things and transform them into economic, social, and cultural values that are beneficial to personal or social development by using their scientific and cultural knowledge and surrounding resources, which is mostly reflected in the innovative entrepreneurial primary stage, as shown in Figure3.Figure 3
Hypothetical model diagram.At the critical period of accelerating the construction of an innovative country, great importance is attached to the cultivation of innovative talents and university students, as highly sophisticated talents who master scientific and cultural knowledge and advanced technology are the main force to achieve this grand strategic goal. Therefore, in the context of “mass entrepreneurship and innovation,” the innovation and entrepreneurship ability of college students should actively respond to the theme of the times and reflect the characteristics of the new era. Therefore, the modernity of the innovation and entrepreneurship ability of college students refers to the fact that college students closely combine their ideal beliefs and life pursuits with national development and social progress, respond to the development requirements of national science and technology innovation, knowledge innovation, and system innovation, as well as strategic opportunities, focus on new technologies, new business modes, and new models, and take the initiative to accumulate and improve or cultivate the corresponding innovation and entrepreneurship ability with external support. This model is used to discuss the influencing factors of college students’ entrepreneurial ability. To ensure that the theoretical framework is scientific and reasonable, based on constructing a theoretical model of college students’ entrepreneurial ability, statistical software is used to conduct quantitative research on college students’ entrepreneurial ability. The new round of business innovation and technological change is reshaping the global economic landscape, and countries all over the world are emphasizing the importance of innovation, but, due to the different social systems, different degrees of development, and different development processes in each country, the requirements for the innovation and entrepreneurship capabilities of college students also vary, and each country should develop corresponding specific strategies in line with national realities and development needs.The innovation and entrepreneurship capabilities that need to be possessed or focused on by innovation and entrepreneurship subjects are different for each stage. Unlike other innovative entrepreneurial groups in society, such as entrepreneurs and migrant workers, the time frame for this particular group of university students is mainly limited to the period during which they attend university and within two years of graduation, and the corresponding entrepreneurial stage is the awakening of consciousness and the start-up phase, which focuses more on the innovative spirit, learning ability, and the knowledge and skills required for the start-up phase [19]. The more specialized and in-depth competencies required in the later development and maturity stages are not the focus of this stage. The innovation and entrepreneurial ability that should be possessed in the awakening stage should be generally oriented to all college students, focusing on the innovation spirit, learning ability, and entrepreneurial personality, while the innovation and entrepreneurial ability that should be possessed in the start-up stage mainly targets a small group of people who transform scientific and technological achievements and start a business, focusing on enterprise management ability and so forth, as shown in Figure 4.Figure 4
Descriptive statistics of organizational mechanisms.The data obtained through survey research in quantitative research is the basis for scientific decision-making, and the selection of appropriate data analysis methods and data analysis software is an effective means to ensure scientific decision-making. According to the research needs and research questions, this paper will use descriptive statistical analysis, factor analysis, hierarchical analysis (AHP), principal component analysis (PCA), and full weight method to determine the weights of innovation and entrepreneurship education ecosystem composition indicators of research universities. Among them, the main methods used in the weight analysis were hierarchical analysis (AHP), principal component analysis (PCA), and the full weight method. In the specific data processing, statistical analysis software programs such as SPSS and AMOS were used as the main analytical tools for data analysis. The specific statistical analysis steps are as follows: Firstly, the research group distributed the questionnaire of “the composition index of innovation and entrepreneurship education ecosystem in research universities” to each university, the subjects were teachers and students, and the survey data were recorded into electronic files. Aiming at the influencing factors, a strategy to improve entrepreneurial ability was proposed, which provided a theoretical basis for the improvement of college students’ entrepreneurial ability. Secondly, according to the questionnaire responses, incomplete responses, regular responses, short responses, and self-contradictory responses were eliminated, and valid questionnaires were obtained. Again, using SPSS statistical software, the obtained data were processed, and descriptive statistical analysis, exploratory factor analysis, and validation factor analysis were conducted on the questionnaire to determine that the questionnaire had good reliability. Finally, the combination of hierarchical analysis, principal component analysis, and full weight method was used to determine the weights of the composition indicators of the innovation and entrepreneurship education ecosystem of research universities to help managers better determine the focus of their work.


## 4. Analysis of Results

### 4.1. Deep Learning Algorithm-Based Results in the Context of the Digital Age

The method of lattice search belongs to the classical exhaustive search method from the mathematical essence. In this paper, all the possible values of the parameters of the network model are combined in a specific permutation, and after they are combined, the result of the permutation is presented in the form of a grid, from which we can see the results that can be combined into these parameters. Then all the parameters in the formed grid can be fed into the network model in the way of combination, and then the whole network model will be trained, the performance of the network model will be continuously evaluated, and the performance parameters after each evaluation will be compared so that the best combination of parameters can be continuously filtered. In the model proposed in this paper, the most important parameters are the number of training sessions, the number of batches, the number of hidden layer neurons, and the step size. Based on practical experience, the benchmark parameters in grid search are determined: the number of training times is 400, the number of batch processes is 20, the number of neurons in the first hidden layer is 16, the number of neurons in the second hidden layer is 8, and the step length is 0.001. Applying deep feedforward neural network for employment classification of secondary school graduates, Figure5 gives the classification accuracy, loss function with iteration for the training and testing process of student employment dataset, and change curve with the number of iterations. From Figure 5, the accuracy of the training set of the student employment dataset rises rapidly in the oscillation until 20 iterations, and then the oscillation of the training set decreases until it stabilizes after 250 iterations; the test set rises rapidly until 15 iterations and then rises slowly and stabilizes. As can be seen from Figure 5, the training set loss decreases rapidly in oscillation until 250 iterations and then gradually stabilizes; the test set loss decreases rapidly until 300 iterations and then slowly decreases and stabilizes.Figure 5
Training set and test set accuracy.Recently, the gradient boosting decision tree model has been performing very well in commercial applications and is a good example of the concept of learning from mistakes. Gradient boosting decision tree is an iterative decision tree algorithm that consists of multiple decision trees and the conclusions of all the trees are accumulated to make the final answer. The obtained results can be used as an important measure to guide the construction of an innovative country; from the background of college entrepreneurship education reform, cultivating college students’ entrepreneurial human capital is an important path to deepen entrepreneurship education reform, implement quality education training goals, achieve comprehensive development of college students, and enhance social adaptability. It was a more generalizable algorithm along with SVM when it was first proposed. The basic idea is to train the newly added weak classifiers based on the negative gradient information of the current model loss function and then combine the trained weak classifiers into the existing model in an accumulative form, and, in each iteration of the computation, the negative gradient is calculated with the current model sample as the goal to train the weak classifiers for merging. The final computed weights are used for the model update. In the experiments, the gradient boosting decision tree model is trained in the training set using XGBClassifier in XGBoost and then a grid search is performed in Scikit-Learn using Research on the estimates of the specified parameters, and all hyperparameters are tested to find the best combination to produce the best results as shown in Figure6.Figure 6
Comparison of prediction results of deep feedforward neural network model with other models.In machine learning, support vector machines (SVMs and support vector networks) are supervised learning models associated with relevant learning algorithms that analyze data and identify patterns for classification and regression analysis. Support vector machines are used to distinguish between various types by finding a hyperplane in a multidimensional space. This hyperplane is determined by the support vector. In the experiments, the support vector machine model is trained in the training set using SVM. Then a grid search is performed in Scikit-Learn using research on the estimates of the specified parameters and all hyperparameters are tested to find the best combination to produce the best results. In this paper, the classical cross-validation method is used to calculate the prediction accuracy of four different models. By comparison, it demonstrated that the deep feedforward neural network model used in this paper has better accuracy than the traditional algorithm. The results prove that the employment prediction algorithm for secondary school graduates based on deep neural networks outperforms other employment prediction algorithms in terms of accuracy, recall, F1 value, and training speed, proving that algorithms based on the current state-of-the-art deep learning have better prediction results compared to traditional machine learning algorithms and finally evaluating the algorithms used in this paper.


### 4.2. Quantitative Results of University Students’ Employability and Entrepreneurship

The results of the evaluation of the degree of importance of the elements of entrepreneurial human capital indicators of university students show that in the structure of entrepreneurial human capital indicators of university students the weight of entrepreneurial consciousness is 0.312, the weight of innovative spirit is 0.198, and the weight of innovative entrepreneurial ability is 0.49. The innovative entrepreneurial ability has the highest weight and is the most important, and the weight and importance of entrepreneurial consciousness and innovative spirit are second to innovative entrepreneurial ability. This is highly consistent with the objective requirement of entrepreneurial activities for practical skills. The weight and importance of entrepreneurial consciousness are second only to innovation and entrepreneurial ability, which indicates the high importance of entrepreneurial consciousness to the cultivation of entrepreneurial human capital among college students. Gradient descent is continuously used to adjust parameters, so that the error between the actual output and the expected output reaches the minimum convergence, and the degree of fit is the best, which greatly improves the robustness of data processing. The weight and importance of innovation spirit are lower than innovation and entrepreneurial ability and entrepreneurial consciousness, which may be because innovation spirit is a more implicit and potential characteristic among human capital characteristics and is less visible compared with innovation and entrepreneurial ability and entrepreneurial consciousness, resulting in the relatively low perception of its importance among survey respondents, as shown in Figure7.Figure 7
Comparison of secondary indicators to which innovation and entrepreneurship belong.In the evaluation of the elements of entrepreneurial consciousness, the weights of entrepreneurial motivation and entrepreneurial confidence are both 0.5, and the weight of each three-level indicator is the same, which indicates that entrepreneurial motivation and entrepreneurial confidence are equally important to entrepreneurial consciousness. In the evaluation of the elements of innovation and entrepreneurship, the weight of the ability to transform results is the highest, and the weights of innovative thinking, knowledge production ability, and technology transformation ability in the three-level indicators are higher and more important. In addition, the weight of innovation ability is second only to the result transformation ability, while the weight of business management ability is relatively low. The analysis may be due to the lack of experience in entrepreneurship and insufficient enterprise management ability among entrepreneurial college students in research universities in China, which causes the survey respondents to pay insufficient attention to business management ability.The fuzzy comprehensive evaluation method is a comprehensive evaluation method based on fuzzy mathematics. This comprehensive evaluation method converts qualitative evaluation into quantitative evaluation according to the affiliation theory of fuzzy mathematics; that is, it uses fuzzy mathematics to make an overall evaluation of things or objects that are subject to multiple factors. It has the characteristics of clear and systematic results, can better solve the fuzzy and difficult to quantify problems, and is suitable for various nondeterministic problems. The empirical evaluation of the level of the entrepreneurial human capital of college students is mainly based on the sample data obtained from the questionnaire survey, and the fuzzy comprehensive evaluation method is used to evaluate the current situation of the level of the entrepreneurial human capital of college students in research universities. The overall level of the entrepreneurial human capital of college students in research universities is measured by the results of the scores of three first-level indicators of entrepreneurial consciousness, innovation spirit, and innovation and entrepreneurial ability, as shown in Figure8.Figure 8
Quantitative indicator judgments.From the overall score of the evaluation index of the entrepreneurial human capital of college students, the overall level of the evaluation index of the entrepreneurial human capital of college students is good (75.68 points). Among them, innovation and entrepreneurial ability and innovation spirit are at a good level, and entrepreneurial consciousness is relatively low and at a passing level. According to the weight distribution of the evaluation, innovation and entrepreneurial ability have the highest weight and the most prominent importance, followed by entrepreneurial consciousness. The speed of back propagation of network errors is reduced. When the stochastic gradient descent method is iterating, only one sample of the network model input samples will be iterated to update the weight, although this will increase the running speed of the network and reduce the amount of calculation. The results of the comprehensive evaluation of the model show that the innovation and entrepreneurship ability, which has a higher weight, has the highest score (79.82 points), which is close to the excellent level. This indicates that the special group of college students in research-oriented universities has a higher quality of innovation and entrepreneurship ability; the requirements and training objectives of research-oriented universities for students’ innovation and entrepreneurship ability are more systematic and professional than other forms of education, and they also put forward more strict requirements on the three core elements of innovation and entrepreneurship knowledge, innovation and entrepreneurship ability, and innovation and entrepreneurship quality. Through the combination of the three elements, the requirements for students’ entrepreneurial ability and entrepreneurial quality are formed, and then entrepreneurial standards are formed. For research universities, it is to cultivate research-oriented entrepreneurial talents with innovative consciousness and creativity. Creativity is expressed in the creative thinking, creative knowledge, and creative ability of research university students in the entrepreneurial process, and innovation is expressed in the innovative spirit, entrepreneurial consciousness, and creative personality in the entrepreneurial process.


## 4.1. Deep Learning Algorithm-Based Results in the Context of the Digital Age

The method of lattice search belongs to the classical exhaustive search method from the mathematical essence. In this paper, all the possible values of the parameters of the network model are combined in a specific permutation, and after they are combined, the result of the permutation is presented in the form of a grid, from which we can see the results that can be combined into these parameters. Then all the parameters in the formed grid can be fed into the network model in the way of combination, and then the whole network model will be trained, the performance of the network model will be continuously evaluated, and the performance parameters after each evaluation will be compared so that the best combination of parameters can be continuously filtered. In the model proposed in this paper, the most important parameters are the number of training sessions, the number of batches, the number of hidden layer neurons, and the step size. Based on practical experience, the benchmark parameters in grid search are determined: the number of training times is 400, the number of batch processes is 20, the number of neurons in the first hidden layer is 16, the number of neurons in the second hidden layer is 8, and the step length is 0.001. Applying deep feedforward neural network for employment classification of secondary school graduates, Figure5 gives the classification accuracy, loss function with iteration for the training and testing process of student employment dataset, and change curve with the number of iterations. From Figure 5, the accuracy of the training set of the student employment dataset rises rapidly in the oscillation until 20 iterations, and then the oscillation of the training set decreases until it stabilizes after 250 iterations; the test set rises rapidly until 15 iterations and then rises slowly and stabilizes. As can be seen from Figure 5, the training set loss decreases rapidly in oscillation until 250 iterations and then gradually stabilizes; the test set loss decreases rapidly until 300 iterations and then slowly decreases and stabilizes.Figure 5
Training set and test set accuracy.Recently, the gradient boosting decision tree model has been performing very well in commercial applications and is a good example of the concept of learning from mistakes. Gradient boosting decision tree is an iterative decision tree algorithm that consists of multiple decision trees and the conclusions of all the trees are accumulated to make the final answer. The obtained results can be used as an important measure to guide the construction of an innovative country; from the background of college entrepreneurship education reform, cultivating college students’ entrepreneurial human capital is an important path to deepen entrepreneurship education reform, implement quality education training goals, achieve comprehensive development of college students, and enhance social adaptability. It was a more generalizable algorithm along with SVM when it was first proposed. The basic idea is to train the newly added weak classifiers based on the negative gradient information of the current model loss function and then combine the trained weak classifiers into the existing model in an accumulative form, and, in each iteration of the computation, the negative gradient is calculated with the current model sample as the goal to train the weak classifiers for merging. The final computed weights are used for the model update. In the experiments, the gradient boosting decision tree model is trained in the training set using XGBClassifier in XGBoost and then a grid search is performed in Scikit-Learn using Research on the estimates of the specified parameters, and all hyperparameters are tested to find the best combination to produce the best results as shown in Figure6.Figure 6
Comparison of prediction results of deep feedforward neural network model with other models.In machine learning, support vector machines (SVMs and support vector networks) are supervised learning models associated with relevant learning algorithms that analyze data and identify patterns for classification and regression analysis. Support vector machines are used to distinguish between various types by finding a hyperplane in a multidimensional space. This hyperplane is determined by the support vector. In the experiments, the support vector machine model is trained in the training set using SVM. Then a grid search is performed in Scikit-Learn using research on the estimates of the specified parameters and all hyperparameters are tested to find the best combination to produce the best results. In this paper, the classical cross-validation method is used to calculate the prediction accuracy of four different models. By comparison, it demonstrated that the deep feedforward neural network model used in this paper has better accuracy than the traditional algorithm. The results prove that the employment prediction algorithm for secondary school graduates based on deep neural networks outperforms other employment prediction algorithms in terms of accuracy, recall, F1 value, and training speed, proving that algorithms based on the current state-of-the-art deep learning have better prediction results compared to traditional machine learning algorithms and finally evaluating the algorithms used in this paper.


## 4.2. Quantitative Results of University Students’ Employability and Entrepreneurship

The results of the evaluation of the degree of importance of the elements of entrepreneurial human capital indicators of university students show that in the structure of entrepreneurial human capital indicators of university students the weight of entrepreneurial consciousness is 0.312, the weight of innovative spirit is 0.198, and the weight of innovative entrepreneurial ability is 0.49. The innovative entrepreneurial ability has the highest weight and is the most important, and the weight and importance of entrepreneurial consciousness and innovative spirit are second to innovative entrepreneurial ability. This is highly consistent with the objective requirement of entrepreneurial activities for practical skills. The weight and importance of entrepreneurial consciousness are second only to innovation and entrepreneurial ability, which indicates the high importance of entrepreneurial consciousness to the cultivation of entrepreneurial human capital among college students. Gradient descent is continuously used to adjust parameters, so that the error between the actual output and the expected output reaches the minimum convergence, and the degree of fit is the best, which greatly improves the robustness of data processing. The weight and importance of innovation spirit are lower than innovation and entrepreneurial ability and entrepreneurial consciousness, which may be because innovation spirit is a more implicit and potential characteristic among human capital characteristics and is less visible compared with innovation and entrepreneurial ability and entrepreneurial consciousness, resulting in the relatively low perception of its importance among survey respondents, as shown in Figure7.Figure 7
Comparison of secondary indicators to which innovation and entrepreneurship belong.In the evaluation of the elements of entrepreneurial consciousness, the weights of entrepreneurial motivation and entrepreneurial confidence are both 0.5, and the weight of each three-level indicator is the same, which indicates that entrepreneurial motivation and entrepreneurial confidence are equally important to entrepreneurial consciousness. In the evaluation of the elements of innovation and entrepreneurship, the weight of the ability to transform results is the highest, and the weights of innovative thinking, knowledge production ability, and technology transformation ability in the three-level indicators are higher and more important. In addition, the weight of innovation ability is second only to the result transformation ability, while the weight of business management ability is relatively low. The analysis may be due to the lack of experience in entrepreneurship and insufficient enterprise management ability among entrepreneurial college students in research universities in China, which causes the survey respondents to pay insufficient attention to business management ability.The fuzzy comprehensive evaluation method is a comprehensive evaluation method based on fuzzy mathematics. This comprehensive evaluation method converts qualitative evaluation into quantitative evaluation according to the affiliation theory of fuzzy mathematics; that is, it uses fuzzy mathematics to make an overall evaluation of things or objects that are subject to multiple factors. It has the characteristics of clear and systematic results, can better solve the fuzzy and difficult to quantify problems, and is suitable for various nondeterministic problems. The empirical evaluation of the level of the entrepreneurial human capital of college students is mainly based on the sample data obtained from the questionnaire survey, and the fuzzy comprehensive evaluation method is used to evaluate the current situation of the level of the entrepreneurial human capital of college students in research universities. The overall level of the entrepreneurial human capital of college students in research universities is measured by the results of the scores of three first-level indicators of entrepreneurial consciousness, innovation spirit, and innovation and entrepreneurial ability, as shown in Figure8.Figure 8
Quantitative indicator judgments.From the overall score of the evaluation index of the entrepreneurial human capital of college students, the overall level of the evaluation index of the entrepreneurial human capital of college students is good (75.68 points). Among them, innovation and entrepreneurial ability and innovation spirit are at a good level, and entrepreneurial consciousness is relatively low and at a passing level. According to the weight distribution of the evaluation, innovation and entrepreneurial ability have the highest weight and the most prominent importance, followed by entrepreneurial consciousness. The speed of back propagation of network errors is reduced. When the stochastic gradient descent method is iterating, only one sample of the network model input samples will be iterated to update the weight, although this will increase the running speed of the network and reduce the amount of calculation. The results of the comprehensive evaluation of the model show that the innovation and entrepreneurship ability, which has a higher weight, has the highest score (79.82 points), which is close to the excellent level. This indicates that the special group of college students in research-oriented universities has a higher quality of innovation and entrepreneurship ability; the requirements and training objectives of research-oriented universities for students’ innovation and entrepreneurship ability are more systematic and professional than other forms of education, and they also put forward more strict requirements on the three core elements of innovation and entrepreneurship knowledge, innovation and entrepreneurship ability, and innovation and entrepreneurship quality. Through the combination of the three elements, the requirements for students’ entrepreneurial ability and entrepreneurial quality are formed, and then entrepreneurial standards are formed. For research universities, it is to cultivate research-oriented entrepreneurial talents with innovative consciousness and creativity. Creativity is expressed in the creative thinking, creative knowledge, and creative ability of research university students in the entrepreneurial process, and innovation is expressed in the innovative spirit, entrepreneurial consciousness, and creative personality in the entrepreneurial process.


## 5. Conclusion

Based on the constructed evaluation indexes of the entrepreneurial human capital of college students in research universities, the overall level of the entrepreneurial human capital of college students in research universities was measured empirically through a fuzzy comprehensive evaluation. To theoretically explore the inner mechanism of entrepreneurial ability generation among college students, the research hypothesis that there is a positive correlation effect between professional ability and entrepreneurial human capital among college students in research universities is proposed and verified by structural equation modeling. The results of the model showed a significant positive correlation between professional competence and entrepreneurial human capital among college students in research universities. In response to the characteristics of data features exhibiting high dimensionality, the sparse connection between features and features, no centralized distribution, and complex and diverse feature attributes, among which discrete features are predominant and continuous features are also interspersed, this paper uses the current state-of-the-art deep learning algorithm to design a deep neural-network-based employment prediction model for secondary school graduates. Firstly, the most important hyperparameters of the network model are mainly the step length, the number of neurons in the hidden layer, the number of training times, the number of batches, and so forth, which are determined by the grid search. Immediately after the grid search, all the hyperparameters of the deep neural network model were combined, and these parameter combinations were fed into the network one by one for training, and the final hyperparameters of the network were obtained through the analysis of the errors and the final training, and, finally, the employment prediction models were compared with those of random forest, XGBoost, and SVM. The results demonstrate that the employment prediction algorithm based on deep neural network for secondary school graduates outperforms other employment prediction algorithms in terms of accuracy, recall, and F1 value, proving that the algorithms based on the current state-of-the-art deep learning have better prediction results compared to the traditional algorithms.



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*Source: 7245465-2021-11-05.xml*