As the academic and social adaptation pressures permeate even college students, mental health problems are beginning to exhibit a tendency of becoming secretive and sophisticated. The existing model of a single psychological counseling intervention lacks information acquisition, risk identification, and collaboration in education, which does not allow constantly monitoring and thoroughly leading students with their mental condition. In case of this scenario, the current paper creates a three-dimensional model of collaborative education of psychology-culture-ideological and political education with the support of an artificial intelligence technology. This model unites the analysis of student behavior and the collaboration mechanism of educational resources to the solution of the systematic functioning of mental health identification and educational intervention. The study uses machine learning algorithms to classify and identify students' psychological states, employs K-means clustering and decision tree classification to achieve hierarchical identification of psychological risks, and combines collaborative filtering recommendation algorithms to match cultural resources and ideological and political courses. Experimental results show that the overall accuracy of the model in identifying psychological risks reaches 0.931, an improvement of approximately 10.6% compared to traditional methods; the student participation rate in cultural activities increased from 46.3% to 68.7%, and the participation rate in ideological and political courses increased from 71.5% to 84.9%; the comprehensive mental health index rose from 0.67 to 0.82, and the rate of proactive psychological help reached 21.6%.

Shangfei Lu. AI-empowered Three-Dimensional Collaborative Mental Health Model of "Psychology-Culture- Ideology and Politics"—A Case Study of Guangxi Universities. Frontiers in Educational Research (2026), Vol. 9, Issue 3: 98-104. https://doi.org/10.25236/FER.2026.090315.

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