Particle Swarm Optimization Neural Network and Its Application in Mental Health Risk Assessment

ABSTRACT Mental health problems are becoming increasingly prominent in modern society. Traditional mental health risk assessment methods are highly subjective and inefficient, making it difficult to meet the needs of complex assessments. This paper focuses on this and proposes a student mental health risk assessment model based on the combination of particle swarm optimization (PSO) and back propagation (BP) neural network. The weight update process of the BP neural network is optimized through the PSO algorithm, aiming to improve the learning efficiency and classification accuracy of the model. The study first outlines the current situation of depression diagnosis, BP theory and PSO algorithm, elaborates on the methodology, covering dataset description, feature engineering and model construction training. In the section of experimental design and result analysis, the evaluation criteria are defined, the performance of the PSO‐BP combination is compared with that of other algorithms, and its effectiveness is verified through application cases. Studies have shown that the PSO‐BP method has been successfully applied to the classification of depression, significantly improving accuracy and having obvious advantages in the task of student mental health assessment. The data processed by feature engineering can effectively train high‐performance classification models. When predicting the mental health status of patients with depression, the PSO‐BP model has high accuracy and generalization ability. It outperforms traditional BP neural networks and other algorithms in multiple evaluation dimensions and has good adaptability and stability. This research provides new ideas and methods for mental health risk assessment and has certain theoretical significance and practical value.