Design and Implementation of an AI and Big Data-Integrated Mental Health Service Platform for College Students

Abstract

With the growing severity of college students' mental health issues, traditional assessment methods face limitations in dynamic monitoring and precise intervention. This study develops an AI and big data-integrated platform for mental health monitoring, assessment, and service. By dynamically collecting and analyzing multi-source data - such as behavioral logs, psychological scales, and social texts - the platform achieves real-time mental state tracking and risk evaluation. Leveraging deep learning and natural language processing, it provides personalized psychological assessment, crisis warnings, and intervention recommendations. Intelligent service modules, including counseling referral and self-help resource push, enhance operational efficiency. Experimental results demonstrate that the platform significantly outperforms conventional methods in assessment accuracy and service response speed, offering an effective intelligent solution for mental health management in universities.