Abstract
In order to solve the problems of high misevaluation rate and low work efficiency in the process of mental health intelligent evaluation, a method of mental health intelligent evaluation system oriented to the decision tree algorithm is proposed. First, the current research status of mental health intelligent evaluation was analyzed and the framework of mental health intelligent evaluation system was constructed. Then, the mental health intelligent evaluation data were collected and the decision tree algorithm was used to analyze and classify the mental health intelligent evaluation data to obtain the mental health intelligent evaluation results. Finally, specific simulation experiments are used to analyze the feasibility and superiority of the mental health intelligent evaluation system. The experimental results show that the recall rate of each system increases with the increasing number of iterations, and the system has the highest recall rate. Also, it is stable after the number of iterations reaches 20, with good recall and adaptive scheduling performance. The recall rate of comparison system 1 and comparison system 2 fluctuates greatly, and the recall rate is lower than that of the system in this paper. It is proved that the method of the mental health intelligent evaluation system of the decision tree algorithm can effectively solve the problem and improve the accuracy of the mental health intelligent evaluation. The efficiency of mental health intelligent evaluation is improved, and the system stability is better, which can meet the actual requirements of current mental health intelligent evaluation.
Highlights
College students are in the late stages of youth development, and their own physical and psychological activities have changed greatly. ey often experience more negative emotions, and their self-regulation and self-control abilities are not strong yet; when dealing with problems such as learning, social interaction, friendship, and love, it often causes intense conflicts of psychological contradictions, resulting in disorders and imbalances in psychological development [1]
In order to improve the effectiveness of mental health intelligent evaluation, a mental health intelligent evaluation system based on a decision tree algorithm is proposed, which can effectively promote the information management of the system, solve the old problems of traditional system resources, give full play to the advantages of the Internet, create a new Internet environment, and effectively solve the psychological problems of contemporary people
In order to enable people to have an accurate understanding of their own mental health and at the same time to promote the scientific and informatization of mental health guidance, a mental health intelligent evaluation system based on the decision tree algorithm is constructed, and scientific mental health evaluation tools are used to comprehensively and objectively reflect the user’s mental health level. e overall structure of the system is shown in Figure 1 [12]
Summary
College students are in the late stages of youth development, and their own physical and psychological activities have changed greatly. ey often experience more negative emotions, and their self-regulation and self-control abilities are not strong yet; when dealing with problems such as learning, social interaction, friendship, and love, it often causes intense conflicts of psychological contradictions, resulting in disorders and imbalances in psychological development [1]. As the backbone of the future development of the country and society, college students will have serious consequences if their development is affected by psychological factors. When they leave the campus and enter the society, they are very susceptible to the influence or temptation of various bad factors, which will be a fatal blow to the social groups that cultivate them or their parents and teachers even irrelevant [2, 3]. Most of the theoretical methods use traditional classification and association analysis methods to study mental health problems.
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