Deep Learning-Based Prediction and Intervention Model for College Students Mental Health Status

Student mental health has become an increasingly pressing issue across academic institutions worldwide, significantly influencing learning outcomes, emotional well-being, and interpersonal relationships within educational ecosystems. The complexity of this issue stems from its multifaceted and dynamic nature, shaped by various psychological, social, and environmental factors. Mental health challenges, including anxiety, depression, and stress, not only hinder academic performance but also disrupt students’ ability to engage meaningfully with peers and faculty, undermining the overall quality of the educational experience. Traditional approaches to assessing student mental health often rely on static surveys, periodic self-reports, or limited clinical evaluations, which are inherently insufficient in capturing the intricate, evolving, and multidimensional nature of mental well-being. Such methods frequently fail to account for critical factors such as daily stress variations, cumulative peer influences, and the temporal dynamics of mental health conditions. These limitations result in a lack of real-time insights, reducing the precision, personalization, and scalability of mental health interventions. Advancements in computational methodologies, particularly in the domains of artificial intelligence (AI), machine learning (ML), and multimodal data analytics, offer transformative potential in addressing these challenges. By integrating diverse data sources such as physiological signals, behavioral patterns, and social network interactions, computational approaches provide a holistic view of students’ mental states. These methods enable the development of adaptive and proactive intervention strategies, tailored to individual needs and responsive to real-time changes in mental health.