232. Construction and effect evaluation of a precise intervention model for student mental health empowered by big data

Abstract Background The prevalence of mental health issues among adolescents continues to rise, significantly impairing academic performance and overall well-being. While big data technologies—characterized by multi-source data integration, precise risk identification, and dynamic tracking—have demonstrated potential in public health and educational management, their application in student mental health lacks systematic quantitative evaluation. To establish a scientific, efficient, and precise intervention framework that enables early risk detection, personalized support, and real-time outcome monitoring, a big data–enabled precision intervention model is proposed. By integrating multi-source data such as psychological questionnaires and academic performance records, the study quantitatively assesses the model’s effectiveness in improving students’ mental health, offering a practical and implementable solution for school-based mental health education. Methods A total of 120 students aged 13–18 from four secondary schools (Symptom Checklist-90 [SCL-90] total score ≥ 160; excluding severe psychiatric or physical disorders) were randomly assigned to an intervention group (n = 60) or a control group (n = 60). Groups showed no significant differences in age, gender, or baseline psychological measures (p>.05). The intervention group received a big data–enabled precision intervention: multi-source data (psychological assessments, campus smart-card logs, academic platforms) were collected and analyzed via machine learning to build a risk-prediction model, which classified students into risk tiers and triggered tailored interventions—low-risk: online psychoeducation; medium-risk: weekly one-on-one counseling; high-risk: plus group activities. The control group received standard care (two semesterly group lectures and on-demand counseling). Assessments using SCL-90, Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), and Resilience Scale (RS) were conducted pre-intervention, at 3 and 6 months, and at 3-month follow-up. Data were analyzed using repeated-measures ANOVA, independent-samples t-tests, and chi-square tests (SPSS 26.0); p