Construction and Empirical Study of a Dynamic Early Warning Model for Psychological Health Risks of College Students Driven by Artificial Intelligence
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Abstract
With the intensification of social competition, academic pressure and lifestyle changes, the mental health problems of college students are showing a trend of high incidence and complexity. Psychological health risk warning and intervention have become key links in ensuring the quality of higher education. The traditional mental health assessment model relies on offline questionnaires and manual screening, which has structural defects such as strong lag, narrow coverage, low recognition accuracy, and insufficient dynamic tracking, making it difficult to meet the needs of large-scale, refined, and forward-looking risk prevention and control. Artificial intelligence technology, with its advantages in data mining, pattern recognition, and deep learning, provides a new technological path for the dynamic early warning of mental health risks among college students. By integrating these advanced algorithms, it is possible to monitor psychological well-being in university mental health management.
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