Research on College Students' Mental Health Issues Based on an Improved BiLSTM Algorithm

With the rise in concerns about the declining mental health of college students, the traditional methods used for evaluation are proving to be unreliable and time-consuming when it comes to providing accurate results. The work revolves around a novel bidirectional long short-term memory neural network (BiLSTM), which is aimed at creating a robust model for the early detection of mental health problems. In this study, we present the BiLSTM+AM model where the attention mechanism is included and different parameter optimization techniques are used to make the model more sensitive to the mood of the emotional text. Besides, we tried to work with data normalization and feature selection as the direct ways of model enhancements. Experimental outcomes demonstrate that the enhanced model outclasses ordinary models in the aspects of accuracy, recall, and F1 score, which are instrumental in the identification of potential mental illness risks and offer a surviving ground for mental health interventions in higher education. Furthermore, the paper provides advances in the method of intelligent psychiatric evaluation and college student studies, and it also presents prospects for future research in relevant fields.