# Evaluation and Analysis of College Students’ Mental Health from the Perspective of Deep Learning

Authors: Yuefen Wang; Chaoqun Ma
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

College students are easily affected by the outside world, which leads to mental health problems, so it is particularly important to accurately evaluate and analyze the mental health status of college students. At present, the evaluation and analysis model of college students’ mental health is inaccurate and inefficient, which cannot analyze the mental health problems of college students. In order to evaluate and analyze the mental health problems of college students more accurately, this paper designs an evaluation and analysis model of college students’ mental health from the perspective of in-depth learning. The accuracy of model evaluation and analysis is improved, and a better comparison result is obtained. Firstly, the BP neural network model was compared with the logistic model and ARIMA model, and the results showed that the accuracy of the BP neural network model was more than 70% in five comparisons and was higher than that of the logistic model and ARIMA models. Second, the BP deep learning method is compared with several conventional methods (KNN, MF, NCF, and DMF) in the comparison phase of the model. The RMSE, MAE, and MAPE of the BP method are lower than those of the other four traditional methods. Finally, in the comparative experiment, the precision and AUC of the BP model are improved by 2%, and the three indicators of precision, recall, and F1 are also higher than those of other models. Through the specific evaluation of the five indicators of the four college students, from the five indicators of psychological adaptability, frustration, emotional stability, temperament, and personality, the mental health of the four college students is better.


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## Body

## 1. Introduction

Psychological health education is an important part of college students’ ideological education. In recent years, with the continuous development of society, the increase in college students has different psychological features in learning, life, interpersonal relationships, etc. after entering the university. This article mainly explores the psychological health assessment and analysis of college students from the perspective of deep learning, using deep learning to better assess and analyze the mental health status of college students. Mental health has constituted a major challenge for the well-being of college students [1]. Using deep learning-based models can better solve the challenges of college students’ mental health assessment and get rid of traditional task methods. Using neural networks and depth learning methods can more accurately assess college students’ mental health status [2]. We use data from China Weibo to study the depth learning method of college students’ mental health [3]. Teaching makes the main channel of college students’ mental health education [4]. Deep neural network performance in the same task is superior to that of other machine learning-based standard classifiers [5]. Deep learning methods can screen the mental health status of college students more accurately and timely, and significantly reduce the consumption of money and energy. Use depth learning images; the study of text can improve the recognition rate [6]. Deep learning is a machine learning method [7]. A number of attractive technologies were promoted. The depth learning model can be used as a supplementary tool for detecting individual mental health status of social media [8]. College students’ mental health is divided into mental illness, paranoia, horror, hostile, depression, anxiety, interpersonal sensitivity, stress, and nine dimensions of somatization [9]. Mental health is one of the factors affecting life aspects [10]. This issue is especially important for college students’ education. Mental health is defined as a state without psychological barriers and happiness [11]. University courses must meet physical and mental health needs, so that students can prevent future issues [12]. Excavation results help to learn more about students’ mental health issues [13]. College students’ mental health is the focus of public and universal attention [14]. Mental health education is an important part of higher education [15].


## 2. Basic Theory

### 2.1. Deep Learning

#### 2.1.1. Connotation of Deep Learning

Deep learning is a process for understanding of learning relative to mechanical, scattered fragmentation [16]. Deep study points to deep information processing, knowledge migration, and problem solving in the real and complex learning situation.


#### 2.1.2. Deep Learning Features

The characteristics of deep learning are shown in Table1.Table 1
Features of deep learning.
Based on real and complex problem situationsDeep learning emphasizes the testing of the mental health of college students in many aspects. Only when students are based in a real and complex situation will they show the truest mental state, so as to more accurately evaluate the mental health of college studentsBased on dialectical thinkingDeep learning emphasizes that when evaluating and analyzing the mental health of college students, it is necessary to link the previous mental health problems of the students and to maintain a dialectical thinking about the mental health problems of the students and form their own knowledge system and logic structureEmphasize the integrated processing of informationDeep learning emphasizes that when evaluating and analyzing college students’ mental health data, it is necessary to combine multiple indicators, multiple information data, and in-depth processing to grasp the structure and diversity of mental health as a whole. It is helpful to cultivate students’ holistic view and connection view, and the results are more accurate when evaluating and analyzing the mental health of college studentsPointing to a higher-order state of mindDeep learning emphasizes that the evaluation and analysis of college students’ mental health problems should not only focus on the surface of the results but also pay attention to the reasons behind the results, identify the root of the problem, and break through to a higher level of thinkingTransfer of intentional knowledgeDeep learning emphasizes the need to learn the transfer of knowledge when evaluating and analyzing the psychological problems of college students. When facing students with mental health problems, it is necessary to combine a variety of knowledge for analysis and apply the learned knowledge to solve problems in different situations. Cultivate the ability to link theory with practice, to learn from one another, and to be flexible and creative


### 2.2. Mental Health of College Students

#### 2.2.1. Concept of Mental Health of College Students

College students’ mental health refers to college students as a special group [16]. Many characteristics of youth performance have a psychological manifestation, but it is not completely equally equal to youth.


#### 2.2.2. Mental Health Standards for College Students

The specific contents of the mental health standards for college students are shown in Table2.Table 2
Mental health standards for college students.
Normal intelligenceNormal intelligence mainly refers to the ability of college students to learn and understand through experience, the ability to acquire and maintain knowledge, the ability to respond quickly and accurately to new situations, and the ability to use reasoning to solve problems effectively. Mentally healthy college students have a strong thirst for knowledge and a strong interest in exploration and can overcome difficulties in the learning process, maintain a certain degree of learning efficiency, and experience happiness and satisfaction from learningEmotional healthThe main signs of emotional health are emotional stability and a happy mood. Mentally healthy college students are mostly optimistic, cheerful, energetic, confident, full of hope in life, good at regulating emotions, able to attract reasonable methods of catharsis, and able to properly express their emotions in different situationsSound-willedSound-willed refers to college students showing a high level of consciousness, determination, tenacity, and self-control. Mentally healthy college students have a certain ability to control their behavior, can correctly understand the purpose and meaning of their actions, have a certain degree of decision-making ability in case of accidents, can persevere, and have a good ability to respond to emergenciesPersonality integrityPersonality is the sum of various psychological characteristics. Personality integrity means that an individual has a sound personality, balanced development of temperament, abilities, beliefs, and outlook in life. Mentally healthy college students can maintain the coordination of thinking, speech, and actions, have a correct self-awareness, have a positive outlook in life, and be able to unify their needs, goals, and actionsSelf-awarenessSelf-consciousness improvement mainly refers to knowing oneself correctly, evaluating oneself, and accepting oneself. Mentally healthy college students can fully understand themselves, put themselves in a correct position, evaluate themselves objectively, and are not arrogant, and they can face reality squarely in the face of setbacks and difficulties, be positive and enterprising, and show self-esteem, self-improvement, and self-control and self-loveInterpersonal harmonyInterpersonal relationship can reflect the situation of college students interacting with others. Mentally healthy college students are willing to interact with others, can get along with others with respect, trust, friendship, tolerance, and understanding, can maintain an independent and complete personality in interactions, evaluate themselves and others objectively, and can maintain a coordinated relationship with the collective, know how to share, and maintain a positive attitude towards communicationNormal social adjustmentAdaptability is an important characteristic of measuring mental health. Mentally healthy college students can maintain good contact with the society and have a clear and correct understanding of the current social situation, and their thoughts and actions can keep up with the pace of development of the times and meet the requirements of society. When you find that your own needs and desires are in conflict with social needs, you can quickly adjust yourself, adapt to social changes, and stay in harmony with societyMental behavior conforms to the age characteristics of college studentsCollege students are at a specific age, and their psychological behaviors should be adapted to their age and roles, and their cognition, emotions, words and deeds, and behavior should all conform to the characteristics of their age. Mentally healthy college students are usually energetic, hardworking, and quick-responsive and like to explore. Being too old, too naive, and too dependent are all signs of being mentally unhealthy


### 2.3. BP Neural Network

This article will use the BP neural network to evaluate and analyze the mental health of college students. The BP neural network contains three parts: input layer, hidden layer, and output layer [17]. It can improve the accuracy of the model and is more accurate when evaluating college students’ mental health issues and is more accurate and faster when analyzing.Suppose that the input layer includes a node, the hidden layer includes a node, and the output layer includes a node [18]. The structure diagram of the BP neural network is shown in Figure 1.Figure 1
BP neural network structure diagram.wherei=1,2,⋯,n; j=1,2,⋯,m; k=1,2,⋯,l.The number of neurons in the hidden layer is(1)Y=X+Z+a.The activation function is a unipolar sigmoid function:(2)fx=11+e−x.Because the sigmoid function is continuously differentiable, we get(3)f′x=fx1−fx.The neuron input information of thej hidden layer is
(4)Aj=∑i=1nvijxi.The neuron output information of thej hidden layer is
(5)yi=f∑j=1mvijxi−bj.Among them,bj represents the threshold of the connection between the input layer and the hidden layer.The neuron input information of thek output layer is
(6)Bk=∑j=1mwjkyj.The neuron output information of thek output layer is
(7)zk=f∑k=1lwjkyj−bk.Among them,bk represents the threshold of the connection between the hidden layer and the output layer.For a training samplexk,zk, the final output value is zk¯=z1k¯,z2k¯,⋯,zlk¯ [18]. Then, the error between the actual value zk and the expected output value zk¯ can be expressed as
(8)Ek=12∑k=1lzk−zk¯2.With backpropagation to the hidden layer and the input layer, you can get(9)Ek=12zk−g∑j=1mwjkyj−bk2=12∑k=1lzk−g∑j=1mwjkg∑i=1nvijxi−bj−bi2.Among them,g represents the inverse function of function f.


### 2.4. BP Algorithm

In the BP algorithm, the iterative methods of parameters are(10)ξi⟵ξi+Δξi.The BP algorithm is composed of two parts: the forward propagation of information and the backward propagation of errors [19]. Assume that the input vector in the BP neural network is
(11)x=x1,x2,⋯,xnT.The hidden layer vector is(12)y=y1,y2,⋯,ynT.The output layer vector is(13)zh¯=z1h¯,z2h¯,⋯,znh¯T.The weight from the input layer to the hidden layer is(14)V=V1,V2,⋯,Vi,⋯,Vm.Where(15)Vi=v1i,v2i,⋯,vniT,1≤i≤m.The weight from the hidden layer to the output layer is(16)W=W1,W2,⋯,Wj,⋯,Wl.Where(17)Wj=w1j,w2j,⋯,wmjT,1≤j≤l.Through the gradient descent method [20], the weight adjustment formula of the hidden layer is
(18)Δvij=−η∂E∂vij,1≤i≤n,1≤j≤m.The weight adjustment formula of the output layer is(19)Δwjk=−η∂E∂wjk,1≤j≤m,1≤k≤l,whereη is the learning rate of error E.The gradient of the output layer thresholdσ is
(20)gk=∂Ec∂σk=∂E∂zkc¯⋅∂zkc¯∂σk=zkc¯1−zkc¯zkc−zkc¯.The gradient of the hidden layer thresholdθ is
(21)hj=∂Ec∂θj=∂Ec∂yj⋅∂yj∂θj=yj1−yi∑k=1lgkwjk.


## 2.1. Deep Learning

### 2.1.1. Connotation of Deep Learning

Deep learning is a process for understanding of learning relative to mechanical, scattered fragmentation [16]. Deep study points to deep information processing, knowledge migration, and problem solving in the real and complex learning situation.


### 2.1.2. Deep Learning Features

The characteristics of deep learning are shown in Table1.Table 1
Features of deep learning.
Based on real and complex problem situationsDeep learning emphasizes the testing of the mental health of college students in many aspects. Only when students are based in a real and complex situation will they show the truest mental state, so as to more accurately evaluate the mental health of college studentsBased on dialectical thinkingDeep learning emphasizes that when evaluating and analyzing the mental health of college students, it is necessary to link the previous mental health problems of the students and to maintain a dialectical thinking about the mental health problems of the students and form their own knowledge system and logic structureEmphasize the integrated processing of informationDeep learning emphasizes that when evaluating and analyzing college students’ mental health data, it is necessary to combine multiple indicators, multiple information data, and in-depth processing to grasp the structure and diversity of mental health as a whole. It is helpful to cultivate students’ holistic view and connection view, and the results are more accurate when evaluating and analyzing the mental health of college studentsPointing to a higher-order state of mindDeep learning emphasizes that the evaluation and analysis of college students’ mental health problems should not only focus on the surface of the results but also pay attention to the reasons behind the results, identify the root of the problem, and break through to a higher level of thinkingTransfer of intentional knowledgeDeep learning emphasizes the need to learn the transfer of knowledge when evaluating and analyzing the psychological problems of college students. When facing students with mental health problems, it is necessary to combine a variety of knowledge for analysis and apply the learned knowledge to solve problems in different situations. Cultivate the ability to link theory with practice, to learn from one another, and to be flexible and creative


## 2.1.1. Connotation of Deep Learning

Deep learning is a process for understanding of learning relative to mechanical, scattered fragmentation [16]. Deep study points to deep information processing, knowledge migration, and problem solving in the real and complex learning situation.


## 2.1.2. Deep Learning Features

The characteristics of deep learning are shown in Table1.Table 1
Features of deep learning.
Based on real and complex problem situationsDeep learning emphasizes the testing of the mental health of college students in many aspects. Only when students are based in a real and complex situation will they show the truest mental state, so as to more accurately evaluate the mental health of college studentsBased on dialectical thinkingDeep learning emphasizes that when evaluating and analyzing the mental health of college students, it is necessary to link the previous mental health problems of the students and to maintain a dialectical thinking about the mental health problems of the students and form their own knowledge system and logic structureEmphasize the integrated processing of informationDeep learning emphasizes that when evaluating and analyzing college students’ mental health data, it is necessary to combine multiple indicators, multiple information data, and in-depth processing to grasp the structure and diversity of mental health as a whole. It is helpful to cultivate students’ holistic view and connection view, and the results are more accurate when evaluating and analyzing the mental health of college studentsPointing to a higher-order state of mindDeep learning emphasizes that the evaluation and analysis of college students’ mental health problems should not only focus on the surface of the results but also pay attention to the reasons behind the results, identify the root of the problem, and break through to a higher level of thinkingTransfer of intentional knowledgeDeep learning emphasizes the need to learn the transfer of knowledge when evaluating and analyzing the psychological problems of college students. When facing students with mental health problems, it is necessary to combine a variety of knowledge for analysis and apply the learned knowledge to solve problems in different situations. Cultivate the ability to link theory with practice, to learn from one another, and to be flexible and creative


## 2.2. Mental Health of College Students

### 2.2.1. Concept of Mental Health of College Students

College students’ mental health refers to college students as a special group [16]. Many characteristics of youth performance have a psychological manifestation, but it is not completely equally equal to youth.


### 2.2.2. Mental Health Standards for College Students

The specific contents of the mental health standards for college students are shown in Table2.Table 2
Mental health standards for college students.
Normal intelligenceNormal intelligence mainly refers to the ability of college students to learn and understand through experience, the ability to acquire and maintain knowledge, the ability to respond quickly and accurately to new situations, and the ability to use reasoning to solve problems effectively. Mentally healthy college students have a strong thirst for knowledge and a strong interest in exploration and can overcome difficulties in the learning process, maintain a certain degree of learning efficiency, and experience happiness and satisfaction from learningEmotional healthThe main signs of emotional health are emotional stability and a happy mood. Mentally healthy college students are mostly optimistic, cheerful, energetic, confident, full of hope in life, good at regulating emotions, able to attract reasonable methods of catharsis, and able to properly express their emotions in different situationsSound-willedSound-willed refers to college students showing a high level of consciousness, determination, tenacity, and self-control. Mentally healthy college students have a certain ability to control their behavior, can correctly understand the purpose and meaning of their actions, have a certain degree of decision-making ability in case of accidents, can persevere, and have a good ability to respond to emergenciesPersonality integrityPersonality is the sum of various psychological characteristics. Personality integrity means that an individual has a sound personality, balanced development of temperament, abilities, beliefs, and outlook in life. Mentally healthy college students can maintain the coordination of thinking, speech, and actions, have a correct self-awareness, have a positive outlook in life, and be able to unify their needs, goals, and actionsSelf-awarenessSelf-consciousness improvement mainly refers to knowing oneself correctly, evaluating oneself, and accepting oneself. Mentally healthy college students can fully understand themselves, put themselves in a correct position, evaluate themselves objectively, and are not arrogant, and they can face reality squarely in the face of setbacks and difficulties, be positive and enterprising, and show self-esteem, self-improvement, and self-control and self-loveInterpersonal harmonyInterpersonal relationship can reflect the situation of college students interacting with others. Mentally healthy college students are willing to interact with others, can get along with others with respect, trust, friendship, tolerance, and understanding, can maintain an independent and complete personality in interactions, evaluate themselves and others objectively, and can maintain a coordinated relationship with the collective, know how to share, and maintain a positive attitude towards communicationNormal social adjustmentAdaptability is an important characteristic of measuring mental health. Mentally healthy college students can maintain good contact with the society and have a clear and correct understanding of the current social situation, and their thoughts and actions can keep up with the pace of development of the times and meet the requirements of society. When you find that your own needs and desires are in conflict with social needs, you can quickly adjust yourself, adapt to social changes, and stay in harmony with societyMental behavior conforms to the age characteristics of college studentsCollege students are at a specific age, and their psychological behaviors should be adapted to their age and roles, and their cognition, emotions, words and deeds, and behavior should all conform to the characteristics of their age. Mentally healthy college students are usually energetic, hardworking, and quick-responsive and like to explore. Being too old, too naive, and too dependent are all signs of being mentally unhealthy


## 2.2.1. Concept of Mental Health of College Students

College students’ mental health refers to college students as a special group [16]. Many characteristics of youth performance have a psychological manifestation, but it is not completely equally equal to youth.


## 2.2.2. Mental Health Standards for College Students

The specific contents of the mental health standards for college students are shown in Table2.Table 2
Mental health standards for college students.
Normal intelligenceNormal intelligence mainly refers to the ability of college students to learn and understand through experience, the ability to acquire and maintain knowledge, the ability to respond quickly and accurately to new situations, and the ability to use reasoning to solve problems effectively. Mentally healthy college students have a strong thirst for knowledge and a strong interest in exploration and can overcome difficulties in the learning process, maintain a certain degree of learning efficiency, and experience happiness and satisfaction from learningEmotional healthThe main signs of emotional health are emotional stability and a happy mood. Mentally healthy college students are mostly optimistic, cheerful, energetic, confident, full of hope in life, good at regulating emotions, able to attract reasonable methods of catharsis, and able to properly express their emotions in different situationsSound-willedSound-willed refers to college students showing a high level of consciousness, determination, tenacity, and self-control. Mentally healthy college students have a certain ability to control their behavior, can correctly understand the purpose and meaning of their actions, have a certain degree of decision-making ability in case of accidents, can persevere, and have a good ability to respond to emergenciesPersonality integrityPersonality is the sum of various psychological characteristics. Personality integrity means that an individual has a sound personality, balanced development of temperament, abilities, beliefs, and outlook in life. Mentally healthy college students can maintain the coordination of thinking, speech, and actions, have a correct self-awareness, have a positive outlook in life, and be able to unify their needs, goals, and actionsSelf-awarenessSelf-consciousness improvement mainly refers to knowing oneself correctly, evaluating oneself, and accepting oneself. Mentally healthy college students can fully understand themselves, put themselves in a correct position, evaluate themselves objectively, and are not arrogant, and they can face reality squarely in the face of setbacks and difficulties, be positive and enterprising, and show self-esteem, self-improvement, and self-control and self-loveInterpersonal harmonyInterpersonal relationship can reflect the situation of college students interacting with others. Mentally healthy college students are willing to interact with others, can get along with others with respect, trust, friendship, tolerance, and understanding, can maintain an independent and complete personality in interactions, evaluate themselves and others objectively, and can maintain a coordinated relationship with the collective, know how to share, and maintain a positive attitude towards communicationNormal social adjustmentAdaptability is an important characteristic of measuring mental health. Mentally healthy college students can maintain good contact with the society and have a clear and correct understanding of the current social situation, and their thoughts and actions can keep up with the pace of development of the times and meet the requirements of society. When you find that your own needs and desires are in conflict with social needs, you can quickly adjust yourself, adapt to social changes, and stay in harmony with societyMental behavior conforms to the age characteristics of college studentsCollege students are at a specific age, and their psychological behaviors should be adapted to their age and roles, and their cognition, emotions, words and deeds, and behavior should all conform to the characteristics of their age. Mentally healthy college students are usually energetic, hardworking, and quick-responsive and like to explore. Being too old, too naive, and too dependent are all signs of being mentally unhealthy


## 2.3. BP Neural Network

This article will use the BP neural network to evaluate and analyze the mental health of college students. The BP neural network contains three parts: input layer, hidden layer, and output layer [17]. It can improve the accuracy of the model and is more accurate when evaluating college students’ mental health issues and is more accurate and faster when analyzing.Suppose that the input layer includes a node, the hidden layer includes a node, and the output layer includes a node [18]. The structure diagram of the BP neural network is shown in Figure 1.Figure 1
BP neural network structure diagram.wherei=1,2,⋯,n; j=1,2,⋯,m; k=1,2,⋯,l.The number of neurons in the hidden layer is(1)Y=X+Z+a.The activation function is a unipolar sigmoid function:(2)fx=11+e−x.Because the sigmoid function is continuously differentiable, we get(3)f′x=fx1−fx.The neuron input information of thej hidden layer is
(4)Aj=∑i=1nvijxi.The neuron output information of thej hidden layer is
(5)yi=f∑j=1mvijxi−bj.Among them,bj represents the threshold of the connection between the input layer and the hidden layer.The neuron input information of thek output layer is
(6)Bk=∑j=1mwjkyj.The neuron output information of thek output layer is
(7)zk=f∑k=1lwjkyj−bk.Among them,bk represents the threshold of the connection between the hidden layer and the output layer.For a training samplexk,zk, the final output value is zk¯=z1k¯,z2k¯,⋯,zlk¯ [18]. Then, the error between the actual value zk and the expected output value zk¯ can be expressed as
(8)Ek=12∑k=1lzk−zk¯2.With backpropagation to the hidden layer and the input layer, you can get(9)Ek=12zk−g∑j=1mwjkyj−bk2=12∑k=1lzk−g∑j=1mwjkg∑i=1nvijxi−bj−bi2.Among them,g represents the inverse function of function f.


## 2.4. BP Algorithm

In the BP algorithm, the iterative methods of parameters are(10)ξi⟵ξi+Δξi.The BP algorithm is composed of two parts: the forward propagation of information and the backward propagation of errors [19]. Assume that the input vector in the BP neural network is
(11)x=x1,x2,⋯,xnT.The hidden layer vector is(12)y=y1,y2,⋯,ynT.The output layer vector is(13)zh¯=z1h¯,z2h¯,⋯,znh¯T.The weight from the input layer to the hidden layer is(14)V=V1,V2,⋯,Vi,⋯,Vm.Where(15)Vi=v1i,v2i,⋯,vniT,1≤i≤m.The weight from the hidden layer to the output layer is(16)W=W1,W2,⋯,Wj,⋯,Wl.Where(17)Wj=w1j,w2j,⋯,wmjT,1≤j≤l.Through the gradient descent method [20], the weight adjustment formula of the hidden layer is
(18)Δvij=−η∂E∂vij,1≤i≤n,1≤j≤m.The weight adjustment formula of the output layer is(19)Δwjk=−η∂E∂wjk,1≤j≤m,1≤k≤l,whereη is the learning rate of error E.The gradient of the output layer thresholdσ is
(20)gk=∂Ec∂σk=∂E∂zkc¯⋅∂zkc¯∂σk=zkc¯1−zkc¯zkc−zkc¯.The gradient of the hidden layer thresholdθ is
(21)hj=∂Ec∂θj=∂Ec∂yj⋅∂yj∂θj=yj1−yi∑k=1lgkwjk.


## 3. Model Design

### 3.1. Research Route

Through the various indicators of psychological problems, the input test samples are evaluated and analyzed, and the data is continuously evaluated and analyzed through comparison, in order to fully understand the mental health of college students. First collect data preprocessing; then, establish college students’ mental health problem model [21]. Use the BP algorithm for learning training, and then, simulate the simulation test of college students’ psychological problems and final comparison results. This paper uses the main problem of students as a sample input. Add personal basic information in the data, and use the BP neural network to establish mental health assessment models. Through the mapping relationship between the influencing factors and psychological issues, the input test samples are studied; only constantly enter and complete training to achieve the purpose of the model established. The research route is shown in Figure 2.Figure 2
Research route.


### 3.2. Mental Health Assessment and Analysis Model

Firstly, the data of college students' mental health is obtained from three aspects of text, image and network evaluation. Among them, text data requires text pretreatment, text data cleaning, and word [22]. Image data requires image pretreatment, image data cleaning, and normalization. Network data requires preprocessing, sequence data cleaning, and modal processing. The sample is then marked, and the samples are divided into training samples and test samples. After, training samples need to be trained to calculate the emotional tendency value in the model and finally combined with the three emotional calculations for the evaluation and analysis of mental health. Training samples in text data use training BII-LSTM methods, training samples in image data use training fine-tuning CNN methods, and network data training samples adopt training HCRF methods, as shown in Figure 3.Figure 3
Mental health assessment and analysis model.


#### 3.2.1. Text Sentiment Calculation

Text information is the basic information of human conveying emotions, expressing ideas [23]. It is also the form of expression of an individual’s psychological state. The formulas are
(22)ot=ht⊕ht′,(23)ht=fW×xt+U×ht−1+b,(24)ht′=fW′×xt+U′×ht−1+b′.Among them, thef function is a LSTM nonlinear function [24]. W, U, W′, and U′ represent the weight of the function, and b, b′ represent the bias of the function.


#### 3.2.2. Image Sentiment Calculation

Image information is supplemented to text information [25]. The formula is
(25)SVi=expVi∑j=1nexpVj.Among them,Vi refers to the i category element of the output vector of the dense connection layer, SVi is the i category emotion probability value of the image, and n represents the positive and negative emotion categories.


## 3.1. Research Route

Through the various indicators of psychological problems, the input test samples are evaluated and analyzed, and the data is continuously evaluated and analyzed through comparison, in order to fully understand the mental health of college students. First collect data preprocessing; then, establish college students’ mental health problem model [21]. Use the BP algorithm for learning training, and then, simulate the simulation test of college students’ psychological problems and final comparison results. This paper uses the main problem of students as a sample input. Add personal basic information in the data, and use the BP neural network to establish mental health assessment models. Through the mapping relationship between the influencing factors and psychological issues, the input test samples are studied; only constantly enter and complete training to achieve the purpose of the model established. The research route is shown in Figure 2.Figure 2
Research route.


## 3.2. Mental Health Assessment and Analysis Model

Firstly, the data of college students' mental health is obtained from three aspects of text, image and network evaluation. Among them, text data requires text pretreatment, text data cleaning, and word [22]. Image data requires image pretreatment, image data cleaning, and normalization. Network data requires preprocessing, sequence data cleaning, and modal processing. The sample is then marked, and the samples are divided into training samples and test samples. After, training samples need to be trained to calculate the emotional tendency value in the model and finally combined with the three emotional calculations for the evaluation and analysis of mental health. Training samples in text data use training BII-LSTM methods, training samples in image data use training fine-tuning CNN methods, and network data training samples adopt training HCRF methods, as shown in Figure 3.Figure 3
Mental health assessment and analysis model.


### 3.2.1. Text Sentiment Calculation

Text information is the basic information of human conveying emotions, expressing ideas [23]. It is also the form of expression of an individual’s psychological state. The formulas are
(22)ot=ht⊕ht′,(23)ht=fW×xt+U×ht−1+b,(24)ht′=fW′×xt+U′×ht−1+b′.Among them, thef function is a LSTM nonlinear function [24]. W, U, W′, and U′ represent the weight of the function, and b, b′ represent the bias of the function.


### 3.2.2. Image Sentiment Calculation

Image information is supplemented to text information [25]. The formula is
(25)SVi=expVi∑j=1nexpVj.Among them,Vi refers to the i category element of the output vector of the dense connection layer, SVi is the i category emotion probability value of the image, and n represents the positive and negative emotion categories.


## 3.2.1. Text Sentiment Calculation

Text information is the basic information of human conveying emotions, expressing ideas [23]. It is also the form of expression of an individual’s psychological state. The formulas are
(22)ot=ht⊕ht′,(23)ht=fW×xt+U×ht−1+b,(24)ht′=fW′×xt+U′×ht−1+b′.Among them, thef function is a LSTM nonlinear function [24]. W, U, W′, and U′ represent the weight of the function, and b, b′ represent the bias of the function.


## 3.2.2. Image Sentiment Calculation

Image information is supplemented to text information [25]. The formula is
(25)SVi=expVi∑j=1nexpVj.Among them,Vi refers to the i category element of the output vector of the dense connection layer, SVi is the i category emotion probability value of the image, and n represents the positive and negative emotion categories.


## 4. Experimental Part

### 4.1. Model Test

First, test the model.


#### 4.1.1. Model Comparison

We compare the BP neural network model with the logistic model and the ARIMA model, respectively, use the three models in the evaluation and analysis of the mental health of college students, and compare the accuracy of the results of the three models. The result is shown in Figure4.Figure 4
Model comparison.Through the comparison of the three models, the accuracy of the BP neural network model is higher than that of the logistic model and the ARIMA model, and the accuracy rate is above 70%, indicating that the model is more suitable for the evaluation and analysis of the mental health of college students.


#### 4.1.2. Model Comparison

In this experiment, the BP deep learning model proposed in this article is compared with several traditional methods (KNN, MF, NCF, and DMF) that are evaluating the mental health of college students. The final experimental results are shown in Figure5.Figure 5
Model comparison.It can be seen from the experimental results that the BP method of deep learning has achieved better evaluation results than the KNN, MF, NCF, and DMF methods. The BP method has a stronger ability to recognize input data and learn data hiding, with flexibility and accuracy.


#### 4.1.3. Comparative Experiment

This article compares the BP depth learning model with other models, and the results are shown in Table3.Table 3
Comparative experiment.
AccuracyPrecisionRecallF1AUCLR0.79900.84000.87280.85610.7557SVM0.83240.84640.92290.88300.7793FM0.84310.85520.92810.89020.7933DNN0.84400.85540.92940.89080.7939DeepFM0.84850.86030.92970.89370.8008PNN0.85760.86460.93920.90040.8098BP0.86680.86800.94990.90710.8179According to the data in the table, a chart is drawn as shown in Figure6.Figure 6
Comparative experiment.It can be seen from the experimental results in Figure6 that the BP model has improved the accuracy and AUC by 2% compared to the best traditional method DNN and has also significantly improved in the three indicators of precision, recall, and F1. The experimental results show that the values of accuracy, precision, recall, F1, and AUCs of the BP depth learning model are 0.8 or more, ranking highest in all models.


### 4.2. Application Effect of College Students’ Mental Health Model

According to this article, the mental health assessment and analysis model of college students is designed and psychologically assesses and analyzes a university student.


#### 4.2.1. Test Results of Five Indicators

Students conduct specific assessments of five indicators: psychological adaptability, frustration tolerance, emotional stability, temperament, and personality, to obtain an individual student’s evaluation results and summarize the overall psychological status trend of students based on various indicators. The conclusion is shown in Table4.Table 4
Test results of five indicators.
Psychological adaptabilityFrustration toleranceEmotional stabilityTemperamentCharacterStudent 18991959883Student 29388979580Student 37897839689Student 47595908977The test results of the five indicators of students showed that the four students had better test results on psychological adaptability, frustration tolerance, emotional stability, temperament, and personality, indicating that the four students had no major problems in mental health, as shown in Figure7.Figure 7
Test results of five indicators for students.


#### 4.2.2. Results of the Emotional Stability Test

In a survey of emotional stability in boys and girls, the results are divided into excellent, good, general, poor, and different, as shown in Table5.Table 5
Emotional stability test results.
Excellent (number/rate)Good (number/rate)General (number/rate)Poor (number/rate)Different (number/rate)Total (number of people)Male130/7.8210/12.61200/71.9120/7.210/0.61670Female40/5.381/1.1560/74.170/9.25/0.7756Total170/7.0291/12.01760/72.5190/7.215/0.62426From the data in Figure8, we can know that 72.5% of the students in the emotional stability test are average, indicating that college students still need to strengthen their emotional stability, and we should help students more in their emotional self-management.Figure 8
Emotional stability test results.


#### 4.2.3. Test Results of Frustration Tolerance

The frustration tolerance test was conducted on boys and girls, and the results were divided into excellent, good, average, poor, and different for statistics. The results are shown in Table6.Table 6
Test results of frustration tolerance.
Excellent (number/rate)Good (number/rate)General (number/rate)Poor (number/rate)Different (number/rate)Total (number of people)Male1500/79.4270/14.390/4.722/1.28/0.41890Female560/76.4120/16.434/5.413/1.86/0.8733Total2060/78.5390/14.8124/5.035/1.314/0.52623From the data in Figure9, we can know that 78.5% of the students in the test of frustration tolerance are excellent, especially boys. This shows that college students have better frustration tolerance, and they are not afraid of difficulties when facing setbacks.Figure 9
Test results of frustration tolerance.


## 4.1. Model Test

First, test the model.


### 4.1.1. Model Comparison

We compare the BP neural network model with the logistic model and the ARIMA model, respectively, use the three models in the evaluation and analysis of the mental health of college students, and compare the accuracy of the results of the three models. The result is shown in Figure4.Figure 4
Model comparison.Through the comparison of the three models, the accuracy of the BP neural network model is higher than that of the logistic model and the ARIMA model, and the accuracy rate is above 70%, indicating that the model is more suitable for the evaluation and analysis of the mental health of college students.


### 4.1.2. Model Comparison

In this experiment, the BP deep learning model proposed in this article is compared with several traditional methods (KNN, MF, NCF, and DMF) that are evaluating the mental health of college students. The final experimental results are shown in Figure5.Figure 5
Model comparison.It can be seen from the experimental results that the BP method of deep learning has achieved better evaluation results than the KNN, MF, NCF, and DMF methods. The BP method has a stronger ability to recognize input data and learn data hiding, with flexibility and accuracy.


### 4.1.3. Comparative Experiment

This article compares the BP depth learning model with other models, and the results are shown in Table3.Table 3
Comparative experiment.
AccuracyPrecisionRecallF1AUCLR0.79900.84000.87280.85610.7557SVM0.83240.84640.92290.88300.7793FM0.84310.85520.92810.89020.7933DNN0.84400.85540.92940.89080.7939DeepFM0.84850.86030.92970.89370.8008PNN0.85760.86460.93920.90040.8098BP0.86680.86800.94990.90710.8179According to the data in the table, a chart is drawn as shown in Figure6.Figure 6
Comparative experiment.It can be seen from the experimental results in Figure6 that the BP model has improved the accuracy and AUC by 2% compared to the best traditional method DNN and has also significantly improved in the three indicators of precision, recall, and F1. The experimental results show that the values of accuracy, precision, recall, F1, and AUCs of the BP depth learning model are 0.8 or more, ranking highest in all models.


## 4.1.1. Model Comparison

We compare the BP neural network model with the logistic model and the ARIMA model, respectively, use the three models in the evaluation and analysis of the mental health of college students, and compare the accuracy of the results of the three models. The result is shown in Figure4.Figure 4
Model comparison.Through the comparison of the three models, the accuracy of the BP neural network model is higher than that of the logistic model and the ARIMA model, and the accuracy rate is above 70%, indicating that the model is more suitable for the evaluation and analysis of the mental health of college students.


## 4.1.2. Model Comparison

In this experiment, the BP deep learning model proposed in this article is compared with several traditional methods (KNN, MF, NCF, and DMF) that are evaluating the mental health of college students. The final experimental results are shown in Figure5.Figure 5
Model comparison.It can be seen from the experimental results that the BP method of deep learning has achieved better evaluation results than the KNN, MF, NCF, and DMF methods. The BP method has a stronger ability to recognize input data and learn data hiding, with flexibility and accuracy.


## 4.1.3. Comparative Experiment

This article compares the BP depth learning model with other models, and the results are shown in Table3.Table 3
Comparative experiment.
AccuracyPrecisionRecallF1AUCLR0.79900.84000.87280.85610.7557SVM0.83240.84640.92290.88300.7793FM0.84310.85520.92810.89020.7933DNN0.84400.85540.92940.89080.7939DeepFM0.84850.86030.92970.89370.8008PNN0.85760.86460.93920.90040.8098BP0.86680.86800.94990.90710.8179According to the data in the table, a chart is drawn as shown in Figure6.Figure 6
Comparative experiment.It can be seen from the experimental results in Figure6 that the BP model has improved the accuracy and AUC by 2% compared to the best traditional method DNN and has also significantly improved in the three indicators of precision, recall, and F1. The experimental results show that the values of accuracy, precision, recall, F1, and AUCs of the BP depth learning model are 0.8 or more, ranking highest in all models.


## 4.2. Application Effect of College Students’ Mental Health Model

According to this article, the mental health assessment and analysis model of college students is designed and psychologically assesses and analyzes a university student.


### 4.2.1. Test Results of Five Indicators

Students conduct specific assessments of five indicators: psychological adaptability, frustration tolerance, emotional stability, temperament, and personality, to obtain an individual student’s evaluation results and summarize the overall psychological status trend of students based on various indicators. The conclusion is shown in Table4.Table 4
Test results of five indicators.
Psychological adaptabilityFrustration toleranceEmotional stabilityTemperamentCharacterStudent 18991959883Student 29388979580Student 37897839689Student 47595908977The test results of the five indicators of students showed that the four students had better test results on psychological adaptability, frustration tolerance, emotional stability, temperament, and personality, indicating that the four students had no major problems in mental health, as shown in Figure7.Figure 7
Test results of five indicators for students.


### 4.2.2. Results of the Emotional Stability Test

In a survey of emotional stability in boys and girls, the results are divided into excellent, good, general, poor, and different, as shown in Table5.Table 5
Emotional stability test results.
Excellent (number/rate)Good (number/rate)General (number/rate)Poor (number/rate)Different (number/rate)Total (number of people)Male130/7.8210/12.61200/71.9120/7.210/0.61670Female40/5.381/1.1560/74.170/9.25/0.7756Total170/7.0291/12.01760/72.5190/7.215/0.62426From the data in Figure8, we can know that 72.5% of the students in the emotional stability test are average, indicating that college students still need to strengthen their emotional stability, and we should help students more in their emotional self-management.Figure 8
Emotional stability test results.


### 4.2.3. Test Results of Frustration Tolerance

The frustration tolerance test was conducted on boys and girls, and the results were divided into excellent, good, average, poor, and different for statistics. The results are shown in Table6.Table 6
Test results of frustration tolerance.
Excellent (number/rate)Good (number/rate)General (number/rate)Poor (number/rate)Different (number/rate)Total (number of people)Male1500/79.4270/14.390/4.722/1.28/0.41890Female560/76.4120/16.434/5.413/1.86/0.8733Total2060/78.5390/14.8124/5.035/1.314/0.52623From the data in Figure9, we can know that 78.5% of the students in the test of frustration tolerance are excellent, especially boys. This shows that college students have better frustration tolerance, and they are not afraid of difficulties when facing setbacks.Figure 9
Test results of frustration tolerance.


## 4.2.1. Test Results of Five Indicators

Students conduct specific assessments of five indicators: psychological adaptability, frustration tolerance, emotional stability, temperament, and personality, to obtain an individual student’s evaluation results and summarize the overall psychological status trend of students based on various indicators. The conclusion is shown in Table4.Table 4
Test results of five indicators.
Psychological adaptabilityFrustration toleranceEmotional stabilityTemperamentCharacterStudent 18991959883Student 29388979580Student 37897839689Student 47595908977The test results of the five indicators of students showed that the four students had better test results on psychological adaptability, frustration tolerance, emotional stability, temperament, and personality, indicating that the four students had no major problems in mental health, as shown in Figure7.Figure 7
Test results of five indicators for students.


## 4.2.2. Results of the Emotional Stability Test

In a survey of emotional stability in boys and girls, the results are divided into excellent, good, general, poor, and different, as shown in Table5.Table 5
Emotional stability test results.
Excellent (number/rate)Good (number/rate)General (number/rate)Poor (number/rate)Different (number/rate)Total (number of people)Male130/7.8210/12.61200/71.9120/7.210/0.61670Female40/5.381/1.1560/74.170/9.25/0.7756Total170/7.0291/12.01760/72.5190/7.215/0.62426From the data in Figure8, we can know that 72.5% of the students in the emotional stability test are average, indicating that college students still need to strengthen their emotional stability, and we should help students more in their emotional self-management.Figure 8
Emotional stability test results.


## 4.2.3. Test Results of Frustration Tolerance

The frustration tolerance test was conducted on boys and girls, and the results were divided into excellent, good, average, poor, and different for statistics. The results are shown in Table6.Table 6
Test results of frustration tolerance.
Excellent (number/rate)Good (number/rate)General (number/rate)Poor (number/rate)Different (number/rate)Total (number of people)Male1500/79.4270/14.390/4.722/1.28/0.41890Female560/76.4120/16.434/5.413/1.86/0.8733Total2060/78.5390/14.8124/5.035/1.314/0.52623From the data in Figure9, we can know that 78.5% of the students in the test of frustration tolerance are excellent, especially boys. This shows that college students have better frustration tolerance, and they are not afraid of difficulties when facing setbacks.Figure 9
Test results of frustration tolerance.


## 5. Conclusion

College students are in an important stage of entering the society, and the mental health of college students is also an important issue facing our education. How to accurately evaluate and analyze the mental health of college students is the theme of this paper. In this paper, in-depth restudy of the perspective of college students’ mental health evaluation and analysis model improves the accuracy and rate of evaluation and analysis and improves work efficiency.The conclusions of the study are as follows:(1)
Through the comparison of the BP neural network model, ARIMA model, and logistic model, the accuracy of the BP neural network model is more than 70% higher than that of the logistic model and ARIMA model(2)
Comparing the BP depth of several traditional methods and learning methods (KNN, MF, NCF, and DMF) in the model stage, in the BP method, the RMSE is 0.88, the MAE is 0.65, and the MAPE is 0.24, which are lower than those in the other four traditional methods(3)
In the comparative experiment, the accuracy of the BP model is higher than that of other models, and the other three indicators are also significantly improved(4)
In this model, through the evaluation of psychological adaptability, frustration tolerance, emotional stability, temperament, and personality of the four college students, it shows that the mental health of the four college students is good, and the scores of each index are all above 70(5)
In order to specifically test the emotional stability and antifrustration ability of college students, we divided college students into two groups, male and female, and tested them, respectively. The results showed that 72.5% of the students performed moderately in the emotional stability test and 78.5% of the students performed well in the frustration tolerance test



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*Source: 5687003-2022-03-19.xml*