Currently, mental health issues among college students have become a complex and pressing reality, necessitating a systematic and scientific intervention strategy for addressing mental health crises among this population. This study first explored a method for obtaining optimal solutions by utilizing an improved artificial bee colony algorithm to derive initial cluster centers, followed by the application of the ABC-SC algorithm in the optimization process of fuzzy clustering algorithms. Subsequently, based on the characteristics of mental health data among college students, a user profile suitable for mental health questionnaire data was established. Finally, a 12-week experimental intervention was conducted on college students from a certain university to experimentally test the effectiveness of the proposed method in improving college students’ mental health status. By using the Symptom Checklist-90 (SCL-90) to assess college students’ mental health, it was found that the proposed method significantly improved symptoms of depression, anxiety, hostility, phobia, somatization, obsessive-compulsive disorder, paranoia, and interpersonal relationships.
