# Research on the Application of Data Mining Technology in College Students’ Mental Health Education in the Network Age

Authors: Xianghai Li
Journal: Security and Communication Networks
 (2022)

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## Abstract

As the marcher of life in the times, the network has penetrated into all fields of life and played an immeasurable role. As a specific contemporary group, college students have a weak sense of self-protection and are very vulnerable to adverse factors. Some colleges and universities are trying to explore various psychological intervention modes in order to conduct psychological counselling and prediction more objectively. Network mental education means that, on the premise of using social networks, educators use psychological science methods to exert a positive impact on all aspects of students' mental health education, so as to promote the development of contemporary mental health of college students under social networks and cultivate the correct mentality of using social networks. It is combined with the established college students' rational correlation analysis system. Based on the collected data and basic information of college students' rational evaluation, the improved mining algorithm is used, and some rules and characteristics of college students' psychological related factors are analyzed from the results, which provides a new idea for college students' rational health education. Aiming at realizing the embedded data mining technology in the psychological management system, this paper expounds in detail the design and implementation of the data mining technology module suitable for the psychological management system and discusses the factors affecting students' mental health.


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## Body

## 1. Introduction

The opportunities and challenges brought by virtual society are the external thrust of the practical development of network mental health education. Carrying out network mental health education is not only the need of the development of the times and the need to solve network psychological problems, but also the inevitable requirement of educational innovation [1]. This method is considered to be an important step in database knowledge discovery [2]. College students usually shoulder the hope of their families and even the country. They have generally received good education and should become the pillars of the country and contribute their intelligence and wisdom to national construction [3]. However, the emergence of psychological problems has led to various tragic events. It is particularly important to find and prevent the mental health problems of college students on time [4]. In such a case, use more scientific technology to ensure the accuracy of data analysis, so that the information provided by the student psychological management system can not only provide decision-making basis for the macromanagement of the school, but also timely assist the psychological counselling teachers to do counselling and intervention work.Literature [5] proposes that data mining is a “discovery” process. We should fully analyze a great quantity of existing data, apply the algorithm to the corresponding data set, analyze the content of the data set, mine the pattern, and summarize the useful structure according to the valuable information contained in the pattern. As a matter of fact, colleges and universities in China began to start paying attention to college students' online mental education and its related working mechanism earlier and gained more experience in solving the mental health problems caused by the Internet [6]. The achievement of the aforementioned objectives might be considered a current requirement in student management in contemporary universities [7]. We can deal with large amounts of fuzzy and random data information in students' mental health files using relevant algorithms, mine potential information, assist psychological counselling teachers in making more scientific and faster psychological judgments and prevention of students, timely conduct psychological counselling and intervention, improve work efficiency, and reduce the occurrence of psychological events to a degree [8].We must recognise the specific requirements for strengthening and improving mental health education for college students, improving the work level of mental health education and psychological counselling, developing a work model appropriate for mental health education for college students in the new era, and continuously strengthening the team of psychological counselling teachers [9].This study addresses the research trend of college students' online mental health education in the future [10] on the basis of sorting out the research state of my country's online mental health education. The association rule algorithm is a hot issue in data mining research. An event's amount of dependency or connection with other occurrences is referred to as association [11]. There are various association rule algorithms, the most famous of which being the Apriori algorithm, whose study and use have had a significant impact on our everyday lives [12]. Based on the original psychological assessment system, this study will develop a psychological data mining model that will mine students' psychological potential information and provide it to psychological counselling personnel in real time [13]. At the same time, the two types of algorithms are used to distinct applications in mental health management systems based on the varied features of association algorithm and decision tree algorithm in data mining. If we can establish psychological files for students at each learning and growth stage and improve the working mechanism for early warning, screening, and tracking of psychological problems, it will encourage psychological counselling teachers to provide timely and effective guidance, intervention, and prevention, which will have a significant positive impact on college students' mental health [14].


## 2. Related Work

Literature [15] points out that network mental health education refers to “the process in which mental health educators use network technology and related functions to help visitors solve psychological problems and improve their psychological quality in a variety of ways.” Literature [16] proposed that network mental health education refers to the psychological education process of making full use of the media platform of the network to carry out diversified psychological knowledge publicity, mental health testing, psychological diagnosis and online psychological counselling. Literature [17] puts forward that foreign developed countries also have strict requirements on the qualifications and experience of practitioners engaged in school mental health education, and they can only work after professional training. From these aspects, we can see that the establishment and management system of American students' psychological archives is systematic and comprehensive, which makes the school mental health education form an environmental atmosphere of concern to the whole society. Based on the understanding of the Internet, [18] believes that the Internet itself should become an important object and content of mental health education.Literature [19] points out that the research on association rule algorithms at home and abroad mostly focuses on the following aspects: multicycle mining algorithm, quantitative updating algorithm, parallel discovery algorithm, mining general or multilayer association rules, mining multivalued attribute association rules, mining association rules based on constraints, and other directions. Literature [20] suggests that the promotion of mental education by computer technology and network technology is born with the development of computer and network. Through computer technology and network technology, the implementation of mental health education can combine sound, video, text, image, and other media by modern means, which widens the way of mental health education. According to [21], a psychological management system based on computer and network technology is one of the methods to reduce resources and enhance efficiency, and most schools and institutions use it. This type of psychological management system necessitates not only relevant psychological knowledge, but also guidance from knowledge such as file management theory and information management theory, as well as familiarity with computer knowledge and the ability to design the psychological management system in detail, which is conducive to the system's development and standardisation, as well as the expansion of user-defined functions and making it more user-friendly. In [22], the link between the arrival of the network environment and changes in people's psychological states has been studied, and it has been discovered that the network environment may clearly create psychological changes in individuals. The notion of network mental health is separated from the concept of network mental disorder in [23], and it is studied as a different topic. However, its definition of network mental health is still based on a conventional mental health notion and thus misses the purpose of the network environment.


## 3. Methodology

### 3.1. Decision Tree Algorithm

Data mining is a frontier discipline in computer science, commonly known as data knowledge discovery [24]. In the face of a large amount of existing data, we should first clarify the mining object, not blindly otherwise we will not get practical information value, or even make wrong decisions. This is because there may be inconsistency, repetition, noise, and other problems in the original data. The data mining process is shown in Figure 1.Figure 1
Process of data mining.The so-called decision tree is a tree structure similar to the flow chart. All data is at the root node. The data is recursively divided into subsets according to the selected attributes. The state of each subset is similar to the target variable of predictable attributes. Each internal node on the tree represents a test on the input attribute, and its edge represents a test result. Each time the tree is split, the impact of the corresponding input attribute on the predictable attribute should be evaluated. In this way, the cycle continues until a specific termination condition is reached, and the decision tree is created. Different from decision-making in business, administration, and other fields, decision-making in data mining is a series of behaviors based on data and covering process and results. Input samples constitute the fundamental concept of the decision-making process, and all “decision trees” are utilised to examine the samples. Because samples are often identical to a big number of “decision trees” in the decision space, 100 percent matching is impossible; hence, the definition of samples with a large number of “decision trees” is treated as a sample characteristic. The benefit is that it may clearly present decision-making outcomes, provide analysis results with typical structural features, and assist in the quantitative study of the psychological issues of the college students to whom the sample belongs.Many people think that data mining is knowledge discovery in data, while others think that data mining is only a basic step in the process of knowledge discovery [25]. Human factors may also lead to deviation and incomplete data records. Students' unclear understanding of the project and their own evaluation will affect the incompleteness of students' information. The process of data mining includes data preparation, data mining, and knowledge representation. The generation of decision tree is generally divided into two stages. The first stage is the construction of decision tree and the second stage is tree pruning. Decision trees are generally constructed by greedy method, recursively from top to bottom. There are two common pruning techniques: prepruning and postpruning. According to the practical application, we choose the postpruning technology to prune the decision tree. The ultimate purpose of pruning is to remove the noise or abnormal data in the decision tree, so that the decision tree has better adaptability, and the data mining rules are more universal. In the process of decision tree construction, there will be noise data and outliers due to errors or missing data in the training set. At the same time, with the increasing amount of data, the number of branches and layers of the decision tree will increase correspondingly, and even the phenomenon of overfitting will appear, which will seriously affect the division of the training set and reduce the reliability of the decision tree. When generating decision trees, the differences between frequently used decision tree algorithms are found in the distinct selection qualities and pruning procedures. The greedy algorithm lies at the heart of decision tree creation. When compared to other data mining algorithms, it has a distinct advantage: it can rapidly develop mining models that are simple to understand. When building branches, the key to a decision tree is the selection of distinct values for record fields. You will need data, which is also known as the relevant attribute, to acquire the value. The data in the psychological management system is distinctive when compared to other sorts of data. Finally, the objective of gathering this information is to analyze and forecast behaviour, and it may then be utilised in the research. Without any additional information foundation or configuration, the construction threshold of the decision tree method is low. It can handle high-dimensional data at the same time, and expressing information in the form of a tree is more natural and understandable. At this time, decision trees are widely used in a variety of disciplines.


### 3.2. Apriori Algorithm

Apriori algorithm is an algorithm with more research and great practical value in association rule algorithms. The algorithm is also widely used in real life. It can be used to explore the relationship between consumers and commodities and mine the characteristics of users' consumption behavior by analyzing users' consumption records. It is used to explore the similarity and relevance of different regions and find relevant information in terms of regions. It is used for text association analysis to seek interrelated content. The main factors restricting the efficiency of Apriori algorithm are as follows: first, there are many scanning times of transaction set. Second, the connection process is cumbersome, the amount of computation is large, and it is very time-consuming. Third, we need to compare k-term frequent sets many times, which is inefficient. The relationship between parameter P and evaluation index TPR is shown in Figure2.Figure 2
Relationship between TPR and P.The relationship between parameter P and evaluation index TNR is shown in Figure3.Figure 3
Relationship between TNR and P.Among them, NS represents the selective retraining classification algorithm without sampling, US represents the selective retraining classification algorithm based on downsampling, and CS represents the selective retraining classification algorithm based on cluster sampling. Because Apriori algorithm and decision tree algorithm deal with discrete data, it is necessary to discretize the continuous data in the data set. Because the traditional Apriori algorithm adopts the recursive mechanism, it has to operate the database once for each frequent itemset. Using matrix based Apriori algorithm for data mining, we must first transform the relevant transaction database into Boolean matrix. Matrix operations are used to modify the process of finding frequent sets. We just have to scan the full database once this way, and then we can abstract it into a matrix operation. We may also utilise certain important qualities to compress the matrix and skillfully employ the logical operation between matrix rows and columns to link in the Apriori algorithm in the process of identifying frequent itemsets, which dramatically enhances the operation efficiency to some level. The association formula is as follows:(1)sX⇒Y=σX∪YM.Usually, confidence is used to judge the credibility of association rules, and its formula is:(2)CX⇒Y=ti:X∪Y⊆ti,ti∈TT.The square error formula is as follows:(3)E=∑i=1k∑p∈cip−mi2.The required expected value can be obtained by the following formula:(4)InfoD=−∑i=1mCi,DDlog2Ci,DD.Set multiple factor values at the same time for correlation analysis as follows:(5)f′=∑iAi|Ai∈f∩x3=gd0.The survey data set is:(6)f=∑iAi|x2=mj1.In this chapter, the improved Apriori algorithm is applied to the correlation analysis of college students' rationality. According to the statistical transaction data set, association rules are mined, and then some potential information is obtained from the analysis, which has guiding and educational significance for guiding college students' rationality and health. The improved algorithm cuts the transaction database into disjoint data blocks according to the logical relationship, mines and finds the frequent item sets from each block in turn, and then merges the obtained frequent item sets to obtain the overall frequent item set. The improved algorithm idea is shown in Figure4.Figure 4
Algorithm improvement idea diagram.The experimental comparison of the three improved algorithms is shown in Figure5.Figure 5
Experiment comparison of three improved algorithms.Combined with the improved method, compared with cmapriori algorithm, pmaapriori algorithm, and MTCA algorithm, they are further improved in compressing matrix, which greatly reduces the time and space overhead of the algorithm. Compared with pmapriori algorithm, MTCA algorithm uses multithreading to transform from thing set to Boolean matrix, increases itemset sorting in the process of finding frequent itemsets, and reduces the generation of many nonfrequent itemsets in the iterative process. According to the nature of apriori, the confidence of the rules followed by the subsequent supersets of the rule must also be greater than the minimum confidence. These are ineffective regulations that may be removed. Data mining technology can fully use its technological capabilities of analyzing data and mining hidden information using a variety of tools in order to uncover patterns from existing data, increase the internal value of existing data, and refine data into knowledge. The object-oriented analysis and design approach is utilised in the system design of demand analysis and embedded data mining technology, and the decision tree algorithm and association rule algorithm in data mining technology are used for data categorization and relationship integration. Figure6 depicts the fluctuation of error with the number of iterations in the data mining clustering method.Figure 6
Variation of error with iteration times.At the same time, the research process will also run through the methods of comparison, comprehensive analysis, theoretical verification, and so on. Based on the original database, the selection, cleaning and conversion methods of students' psychological archives data are established. The Apriori algorithm is applied to the analysis of students' psychological problems. Through years of information application and data accumulation, the importance of using data mining technology to apply students' psychological data has become more and more prominent, as well as the correlation analysis of some psychological or behavioral phenomena, so as to grasp the students' psychological change tendency as soon as possible and help psychological counselling teachers better prevent and dredge psychological problems. The memory consumption of data streams with different lengths of decision tree and Apriori algorithm is shown in Figure7.Figure 7
Memory consumed by data streams of different lengths.


## 3.1. Decision Tree Algorithm

Data mining is a frontier discipline in computer science, commonly known as data knowledge discovery [24]. In the face of a large amount of existing data, we should first clarify the mining object, not blindly otherwise we will not get practical information value, or even make wrong decisions. This is because there may be inconsistency, repetition, noise, and other problems in the original data. The data mining process is shown in Figure 1.Figure 1
Process of data mining.The so-called decision tree is a tree structure similar to the flow chart. All data is at the root node. The data is recursively divided into subsets according to the selected attributes. The state of each subset is similar to the target variable of predictable attributes. Each internal node on the tree represents a test on the input attribute, and its edge represents a test result. Each time the tree is split, the impact of the corresponding input attribute on the predictable attribute should be evaluated. In this way, the cycle continues until a specific termination condition is reached, and the decision tree is created. Different from decision-making in business, administration, and other fields, decision-making in data mining is a series of behaviors based on data and covering process and results. Input samples constitute the fundamental concept of the decision-making process, and all “decision trees” are utilised to examine the samples. Because samples are often identical to a big number of “decision trees” in the decision space, 100 percent matching is impossible; hence, the definition of samples with a large number of “decision trees” is treated as a sample characteristic. The benefit is that it may clearly present decision-making outcomes, provide analysis results with typical structural features, and assist in the quantitative study of the psychological issues of the college students to whom the sample belongs.Many people think that data mining is knowledge discovery in data, while others think that data mining is only a basic step in the process of knowledge discovery [25]. Human factors may also lead to deviation and incomplete data records. Students' unclear understanding of the project and their own evaluation will affect the incompleteness of students' information. The process of data mining includes data preparation, data mining, and knowledge representation. The generation of decision tree is generally divided into two stages. The first stage is the construction of decision tree and the second stage is tree pruning. Decision trees are generally constructed by greedy method, recursively from top to bottom. There are two common pruning techniques: prepruning and postpruning. According to the practical application, we choose the postpruning technology to prune the decision tree. The ultimate purpose of pruning is to remove the noise or abnormal data in the decision tree, so that the decision tree has better adaptability, and the data mining rules are more universal. In the process of decision tree construction, there will be noise data and outliers due to errors or missing data in the training set. At the same time, with the increasing amount of data, the number of branches and layers of the decision tree will increase correspondingly, and even the phenomenon of overfitting will appear, which will seriously affect the division of the training set and reduce the reliability of the decision tree. When generating decision trees, the differences between frequently used decision tree algorithms are found in the distinct selection qualities and pruning procedures. The greedy algorithm lies at the heart of decision tree creation. When compared to other data mining algorithms, it has a distinct advantage: it can rapidly develop mining models that are simple to understand. When building branches, the key to a decision tree is the selection of distinct values for record fields. You will need data, which is also known as the relevant attribute, to acquire the value. The data in the psychological management system is distinctive when compared to other sorts of data. Finally, the objective of gathering this information is to analyze and forecast behaviour, and it may then be utilised in the research. Without any additional information foundation or configuration, the construction threshold of the decision tree method is low. It can handle high-dimensional data at the same time, and expressing information in the form of a tree is more natural and understandable. At this time, decision trees are widely used in a variety of disciplines.


## 3.2. Apriori Algorithm

Apriori algorithm is an algorithm with more research and great practical value in association rule algorithms. The algorithm is also widely used in real life. It can be used to explore the relationship between consumers and commodities and mine the characteristics of users' consumption behavior by analyzing users' consumption records. It is used to explore the similarity and relevance of different regions and find relevant information in terms of regions. It is used for text association analysis to seek interrelated content. The main factors restricting the efficiency of Apriori algorithm are as follows: first, there are many scanning times of transaction set. Second, the connection process is cumbersome, the amount of computation is large, and it is very time-consuming. Third, we need to compare k-term frequent sets many times, which is inefficient. The relationship between parameter P and evaluation index TPR is shown in Figure2.Figure 2
Relationship between TPR and P.The relationship between parameter P and evaluation index TNR is shown in Figure3.Figure 3
Relationship between TNR and P.Among them, NS represents the selective retraining classification algorithm without sampling, US represents the selective retraining classification algorithm based on downsampling, and CS represents the selective retraining classification algorithm based on cluster sampling. Because Apriori algorithm and decision tree algorithm deal with discrete data, it is necessary to discretize the continuous data in the data set. Because the traditional Apriori algorithm adopts the recursive mechanism, it has to operate the database once for each frequent itemset. Using matrix based Apriori algorithm for data mining, we must first transform the relevant transaction database into Boolean matrix. Matrix operations are used to modify the process of finding frequent sets. We just have to scan the full database once this way, and then we can abstract it into a matrix operation. We may also utilise certain important qualities to compress the matrix and skillfully employ the logical operation between matrix rows and columns to link in the Apriori algorithm in the process of identifying frequent itemsets, which dramatically enhances the operation efficiency to some level. The association formula is as follows:(1)sX⇒Y=σX∪YM.Usually, confidence is used to judge the credibility of association rules, and its formula is:(2)CX⇒Y=ti:X∪Y⊆ti,ti∈TT.The square error formula is as follows:(3)E=∑i=1k∑p∈cip−mi2.The required expected value can be obtained by the following formula:(4)InfoD=−∑i=1mCi,DDlog2Ci,DD.Set multiple factor values at the same time for correlation analysis as follows:(5)f′=∑iAi|Ai∈f∩x3=gd0.The survey data set is:(6)f=∑iAi|x2=mj1.In this chapter, the improved Apriori algorithm is applied to the correlation analysis of college students' rationality. According to the statistical transaction data set, association rules are mined, and then some potential information is obtained from the analysis, which has guiding and educational significance for guiding college students' rationality and health. The improved algorithm cuts the transaction database into disjoint data blocks according to the logical relationship, mines and finds the frequent item sets from each block in turn, and then merges the obtained frequent item sets to obtain the overall frequent item set. The improved algorithm idea is shown in Figure4.Figure 4
Algorithm improvement idea diagram.The experimental comparison of the three improved algorithms is shown in Figure5.Figure 5
Experiment comparison of three improved algorithms.Combined with the improved method, compared with cmapriori algorithm, pmaapriori algorithm, and MTCA algorithm, they are further improved in compressing matrix, which greatly reduces the time and space overhead of the algorithm. Compared with pmapriori algorithm, MTCA algorithm uses multithreading to transform from thing set to Boolean matrix, increases itemset sorting in the process of finding frequent itemsets, and reduces the generation of many nonfrequent itemsets in the iterative process. According to the nature of apriori, the confidence of the rules followed by the subsequent supersets of the rule must also be greater than the minimum confidence. These are ineffective regulations that may be removed. Data mining technology can fully use its technological capabilities of analyzing data and mining hidden information using a variety of tools in order to uncover patterns from existing data, increase the internal value of existing data, and refine data into knowledge. The object-oriented analysis and design approach is utilised in the system design of demand analysis and embedded data mining technology, and the decision tree algorithm and association rule algorithm in data mining technology are used for data categorization and relationship integration. Figure6 depicts the fluctuation of error with the number of iterations in the data mining clustering method.Figure 6
Variation of error with iteration times.At the same time, the research process will also run through the methods of comparison, comprehensive analysis, theoretical verification, and so on. Based on the original database, the selection, cleaning and conversion methods of students' psychological archives data are established. The Apriori algorithm is applied to the analysis of students' psychological problems. Through years of information application and data accumulation, the importance of using data mining technology to apply students' psychological data has become more and more prominent, as well as the correlation analysis of some psychological or behavioral phenomena, so as to grasp the students' psychological change tendency as soon as possible and help psychological counselling teachers better prevent and dredge psychological problems. The memory consumption of data streams with different lengths of decision tree and Apriori algorithm is shown in Figure7.Figure 7
Memory consumed by data streams of different lengths.


## 4. Result Analysis and Discussion

### 4.1. The Necessity of Developing Network Mental Health Education

Network education has the following characteristics: (1) the “virtuality” of the network makes mental health education easier to implement; (2) it improves the effect of mental health education; (3) it expands the path of mental health education. Because the psychological management system has not been applied to colleges and universities for a long time, for different levels of schools and students, the supporting analysis and processing methods in the psychological management system are not perfect, so it is necessary to apply data mining technology to the student psychological management system. Using classification to analyze and mine a large amount of data in the original database, find out their common ground, classify them, and then find out the internal relations among them according to the classification results. By using classification rules, different mental health education can be adopted for different students, which can further improve the effect and quality of mental health education. (1) Network mental health education is the product of social development trend. With the development of information technology, using the Internet has become one of the important ways of people's daily life. (2) The development of online mental education is conducive to solving more problems related to online psychology. At present, due to the imperfect laws and regulations on the Internet, there are many factors that easily affect visitors' psychological and emotional experience on the Internet and lead to mental health problems such as cognitive imbalance, communication barriers, abnormal psychology, network syndrome, and even Internet addiction. Therefore, strengthening online mental health education can prevent and correct related online psychological problems. (3) Internet access has become an important way for college students to engage in scientific research, acquire knowledge, understand current affairs, exchange feelings, inquire information and have fun, and become an indispensable part of their study and life. In this situation, if we only rely on the traditional mental health education model, we will be greatly behind the social development and out of touch with the requirements of the times. Therefore, we must blaze new trails, make beneficial explorations on the network mental health education in colleges and universities, and open up new ways of mental education.Use decision tree and Apriori algorithm to mine and analyze psychological assessment data to discover its potential information, help psychological counselors to classify students' psychological status, predict psychological status, etc., and assist in timely detection, prevention, and guidance of students' mental health problems. Use the relevant algorithms in the data mining technology to mine these data and find valuable information, which can better carry out the corresponding mental health education work. Data mining not only gives more relevant data for better psychological counselling and counsellor work in colleges and universities, but it also allows institutions to extract prospective information resources for in-depth academic study. The integration of data mining technology into a college's psychological management system increases not just the scientific character of mental health education, but also its efficacy. Establishing a proper view on life and beliefs before leaving university has a significant influence on family life after graduation. A person's mental health has a direct impact on their worldview and viewpoint on life. As a result, research into the rational correlation variables of college students may help to alleviate future family and societal conflicts. The network can integrate mental health, hygiene, and skill training knowledge into the website, allowing college students to conduct independent psychological education efficiently and conveniently; it can also allow students who do not want to be interviewed to receive counselling anonymously, reducing psychological pressure and burden, and ensuring the effectiveness of psychological counselling. To avoid and repair network mental disorders, network mental education should be enhanced.


### 4.2. Measures Taken to Carry Out Network Mental Health Education

In the network age, the educational subject has a trend of “nonsubjectivity.” The educational subject is no longer the authority to instill ideas, but the subject to manufacture, disseminate, and monitor network information. It has the dual identity of information disseminator and ideological guide. Educators and educational objects are equal. The traditional relationship between “teaching” and “being taught” is no longer obvious. On the one hand, establish and improve mental health of college students archives. Improving college students' individual psychological files through computer network, recording students' psychological change trajectory, and individual dynamic monitoring can form an important basis for universities to effectively carry out online mental education. With the help of psychological testing tools, psychological archives can restore and record the real mental health status and emotional personality of college students. On the other hand, network psychological counselling is the main way of network mental health education. Network psychological consultation mainly uses the form of words for communication.The system will provide early warning for the most common mental health problems among students, as well as background information on some students' growth processes, and will remind psychological counselors and other student managers to strengthen intervention and guidance to students in some areas, and to assist them in alleviating or eliminating various degrees of psychological distress in various ways, in order to promote overall improvement of students' psychol. To teach mental health of college students in a network setting, mental health educators should begin with network morality. The goal of developing college students' psychological quality and improving their psychological educators should also be taken as their psychological educators. From the process of mental health education, integrate various elements in order to improve the overall mental health level of college students. Only in this way can we improve the mental health level of college students as a whole and individual college students and fundamentally prevent the emergence of mental health diseases. Different personalities in the network environment and the real environment may sometimes be harmonious and unified, and sometimes they may be seriously opposed, resulting in serious mental health obstacles. This requires college students to maintain the personality unity of network and reality. School education should not only meet the requirements of college students' mental health without problems and psychological obstacles, but also use network tools and adopt various new methods and models to improve college students' psychological acceptance ability, help others to help themselves, and constantly tap their potential.


## 4.1. The Necessity of Developing Network Mental Health Education

Network education has the following characteristics: (1) the “virtuality” of the network makes mental health education easier to implement; (2) it improves the effect of mental health education; (3) it expands the path of mental health education. Because the psychological management system has not been applied to colleges and universities for a long time, for different levels of schools and students, the supporting analysis and processing methods in the psychological management system are not perfect, so it is necessary to apply data mining technology to the student psychological management system. Using classification to analyze and mine a large amount of data in the original database, find out their common ground, classify them, and then find out the internal relations among them according to the classification results. By using classification rules, different mental health education can be adopted for different students, which can further improve the effect and quality of mental health education. (1) Network mental health education is the product of social development trend. With the development of information technology, using the Internet has become one of the important ways of people's daily life. (2) The development of online mental education is conducive to solving more problems related to online psychology. At present, due to the imperfect laws and regulations on the Internet, there are many factors that easily affect visitors' psychological and emotional experience on the Internet and lead to mental health problems such as cognitive imbalance, communication barriers, abnormal psychology, network syndrome, and even Internet addiction. Therefore, strengthening online mental health education can prevent and correct related online psychological problems. (3) Internet access has become an important way for college students to engage in scientific research, acquire knowledge, understand current affairs, exchange feelings, inquire information and have fun, and become an indispensable part of their study and life. In this situation, if we only rely on the traditional mental health education model, we will be greatly behind the social development and out of touch with the requirements of the times. Therefore, we must blaze new trails, make beneficial explorations on the network mental health education in colleges and universities, and open up new ways of mental education.Use decision tree and Apriori algorithm to mine and analyze psychological assessment data to discover its potential information, help psychological counselors to classify students' psychological status, predict psychological status, etc., and assist in timely detection, prevention, and guidance of students' mental health problems. Use the relevant algorithms in the data mining technology to mine these data and find valuable information, which can better carry out the corresponding mental health education work. Data mining not only gives more relevant data for better psychological counselling and counsellor work in colleges and universities, but it also allows institutions to extract prospective information resources for in-depth academic study. The integration of data mining technology into a college's psychological management system increases not just the scientific character of mental health education, but also its efficacy. Establishing a proper view on life and beliefs before leaving university has a significant influence on family life after graduation. A person's mental health has a direct impact on their worldview and viewpoint on life. As a result, research into the rational correlation variables of college students may help to alleviate future family and societal conflicts. The network can integrate mental health, hygiene, and skill training knowledge into the website, allowing college students to conduct independent psychological education efficiently and conveniently; it can also allow students who do not want to be interviewed to receive counselling anonymously, reducing psychological pressure and burden, and ensuring the effectiveness of psychological counselling. To avoid and repair network mental disorders, network mental education should be enhanced.


## 4.2. Measures Taken to Carry Out Network Mental Health Education

In the network age, the educational subject has a trend of “nonsubjectivity.” The educational subject is no longer the authority to instill ideas, but the subject to manufacture, disseminate, and monitor network information. It has the dual identity of information disseminator and ideological guide. Educators and educational objects are equal. The traditional relationship between “teaching” and “being taught” is no longer obvious. On the one hand, establish and improve mental health of college students archives. Improving college students' individual psychological files through computer network, recording students' psychological change trajectory, and individual dynamic monitoring can form an important basis for universities to effectively carry out online mental education. With the help of psychological testing tools, psychological archives can restore and record the real mental health status and emotional personality of college students. On the other hand, network psychological counselling is the main way of network mental health education. Network psychological consultation mainly uses the form of words for communication.The system will provide early warning for the most common mental health problems among students, as well as background information on some students' growth processes, and will remind psychological counselors and other student managers to strengthen intervention and guidance to students in some areas, and to assist them in alleviating or eliminating various degrees of psychological distress in various ways, in order to promote overall improvement of students' psychol. To teach mental health of college students in a network setting, mental health educators should begin with network morality. The goal of developing college students' psychological quality and improving their psychological educators should also be taken as their psychological educators. From the process of mental health education, integrate various elements in order to improve the overall mental health level of college students. Only in this way can we improve the mental health level of college students as a whole and individual college students and fundamentally prevent the emergence of mental health diseases. Different personalities in the network environment and the real environment may sometimes be harmonious and unified, and sometimes they may be seriously opposed, resulting in serious mental health obstacles. This requires college students to maintain the personality unity of network and reality. School education should not only meet the requirements of college students' mental health without problems and psychological obstacles, but also use network tools and adopt various new methods and models to improve college students' psychological acceptance ability, help others to help themselves, and constantly tap their potential.


## 5. Conclusions

This paper introduces the definition, main tasks, process, and common data mining technologies of data mining, introduces the concept, classification, and algorithm of association rules and classification analysis in detail, compresses the two data mining technologies, and determines the matrix based Apriori algorithm and decision tree algorithm used in this research. This paper introduces two algorithms for solving college students' psychological problems. In the new development stage, based on the in-depth analysis of the educational objects, the research of network education strengthens the research of basic theory, vigorously promotes the integration of network mental health education methods and approaches and constructs a three-dimensional network mental health education system on this basis, so as to provide scientific guidance for the practice of network mental health education. The results show that the management system follows the guidance of doing a good job in students and adopts the correct technical route and reasonable development mode, which can strengthen the function of psychological management system and improve the work efficiency of relevant staff. It is conducive to the establishment of a stable, reliable, efficient, and scalable psychological assistance system and is more conducive to the establishment of a harmonious campus.



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*Source: 4449066-2022-03-21.xml*