Early Warning Mechanism of College Students’ Psychological Crisis Based on Clustering Extraction Algorithm

This article proposes a psychological crisis warning mechanism for college students based on clustering extraction algorithm. This mechanism monitors and analyzes the psychological state of college students, and uses clustering algorithms to classify students according to their psychological state, thereby achieving early warning of psychological crises. The specific implementation process includes three steps: first, collect students’ psychological state data; Then, use clustering algorithms to divide students into different groups; Finally, evaluate the psychological crisis risk of students based on the characteristic values of each group, and provide timely intervention for high-risk students. The experimental results indicate that this mechanism can effectively improve the accuracy of early warning for college students’ psychological crisis, and provide timely help and support for students’ psychological problems. This study has certain reference value for improving the mental health level of college students.