The Design of College Students’ Mental Health Analysis System Based on Human-Computer Interaction

Abstract

This paper presents a machine learning-based system for analyzing the mental health of college students. The system utilizes data mining techniques to analyze and process psychological data, enabling personalized mental health assessment and guidance. Firstly, the paper introduces the basic concepts and steps of data mining, as well as the system architecture of the data warehouse. Then, it discusses the methods of incorporating clustering, anomaly mining algorithms, and association rules into the analysis of psychological data in college students. Next, the paper provides a detailed description of the overall structure and workflow of the machine learning-based system for analyzing the mental health of college students. The mental health assessment model utilizes evaluation criteria determination and weight assignment methods. Finally, the accuracy and effectiveness of the system are validated through performance testing. This system provides college students with scientifically feasible mental health assessment and guidance, which has significant practical implications for addressing mental health issues among college students.