A Data-Driven Analysis of Mental Health Status and Early Warning System for Vocational College Students

Based on big data analysis methods, this study conducted a systematic investigation and analysis of mental health conditions among 379 students at a vocational college in Shenzhen. Using quantitative assessment tools, the study analyzed multiple dimensions including cognition, thinking patterns, personality, emotions, and volitional behavior. Results showed that 55.94% of students maintained good mental health, with an overall average score of 86.6 (SD = 14.27). The emotional dimension demonstrated the strongest performance (71.50% reaching good levels), while the thinking dimension required particular attention (10.03% needing improvement). The study found that 19.53% of students exhibited varying degrees of depressive tendencies, and 39.58% needed improvements in social support levels. A significant positive correlation was observed between emotional state and social support (r = 0.68, p < 0.01). Based on these findings, this study constructed a warning index calculation model, providing data support and practical references for implementing targeted mental health interventions.