College student mental health early warning algorithm based on distributed multi-agent system.

With the rapid development of society and the intensification of employment competitiveness, the mental health problems of college students have attracted widespread attention. The rapid increase in mental health problems among college students has brought great challenges to college students and university education management institutions. Therefore, the development of an effective college student mental health early warning algorithm and corresponding systems to support mental health management has become an urgent problem to be solved. In view of the above problems, this paper proposes a deep learning algorithm for college students' mental health early warning model, which collects a large amount of mental health data, combines the advantages of distributed multi-agent systems, divides the data into multiple data subsets, and each agent learns and extracts the characteristics of potential mental health problems according to the corresponding data subset, and finally, the final early warning scheme is obtained by integrating the learning results of each agent. Experimental results show that the algorithm has high accuracy and efficiency, can accurately identify potential mental health problems of college students, and warn in advance. Compared with traditional prediction methods, the deep learning algorithm for college students' mental health warning has better scalability when processing large-scale data. In addition, deep learning algorithms can quickly adapt to the differences between different time periods and individuals, providing more comprehensive support for college students' mental health management. [ABSTRACT FROM AUTHOR]