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

The study introduces a novel metric for assessing college students' mental health by incorporating temporal perception with a hybrid clustering algorithm, combining the fireworks algorithm and K-means clustering. This approach addresses the limitations of traditional indices that focus mainly on physical aspects, offering a more comprehensive evaluation of psychological well-being. The new composite metric effectively captures the dynamic mental health changes in students, achieving over 90% accuracy across various assessment stages, surpassing existing methods in precision and simplicity. This advancement enhances the understanding of students' mental health amid changing public health conditions.