Research on Mental Health Situations of College Students Based on Machine Learning Algorithm

In the context of rising societal pressures and mental health challenges among college students, a profound understanding of their psychological well-being is crucial. This research employs machine learning algorithms to analyze the mental health situations of college students, aiming to lay groundwork for effective interventions and support systems. Utilizing advanced machine learning techniques, we dissect large-scale data related to students' mental health, uncovering critical factors and trends. Our predictive models successfully anticipate students' mental health status, thereby pinpointing potential risks and forming the basis for personalized care. Significant findings of the study include the identification of a strong correlation between academic stress, social interactions, and mental health outcomes. We also discovered pronounced differences in mental health trajectories based on gender, academic year, and personal background, emphasizing the need for tailored intervention strategies. For instance, freshmen exhibited higher levels of stress but were more receptive to counselling, while students from diverse backgrounds showed increased susceptibility to environmental shifts, necessitating specialized support structures. By leveraging these insights, educational institutions can craft targeted mental health programs, enhancing student well-being, reducing stress, and promoting comprehensive academic and personal development. Our research not only offers a scientific foundation for these strategies but also underscores the critical role of machine learning in mental health assessments and intervention planning.