Assessment of College Students’ Mental Health Status Based on Temporal Perception and Hybrid Clustering Algorithm Under the Impact of Public Health Events

The study presents a novel method for assessing college students' mental health during public health crises by integrating temporal perception into a hybrid clustering algorithm that combines the fireworks algorithm with K-means. Tested on 200 students, this approach improves the accuracy and reliability of mental health assessments by overcoming traditional K-means limitations. The model categorizes mental health into four grades and emphasizes the importance of economic support, psychological counseling, and family involvement. The findings suggest that this method effectively captures dynamic mental health features, offering a promising tool for enhancing psychological guidance and interventions in educational settings.