Students’ mental health problems are increasingly becoming an important part of the educational and teaching process in colleges and universities. In this paper, we collect students’ psychological data through the students’ mental health early warning system and preprocess the data through data cleaning and other data. The features of the processed mental health data are extracted using Global Chaos Bat Based Algorithm (GCBA). Construct a mental health early warning system for college students and build a decision tree model into the system for categorizing students’ mental health status. The performance of the decision tree model in this paper is verified by evaluating the finger with other models and comparing the actual classification prediction results, constructing the decision tree model with the psychological condition of interpersonal relationship of college students as an example, and conducting the visualization analysis of the decision tree. Independent sample t-test is conducted on three measures such as using the mental health early warning system constructed in this paper, and according to the results, the application of the system in this paper highlights the role of the enhancement of the level of students’ mental health and the significant improvement of depression and other psychological conditions.