Neural Networks based Identification of Psychological Health Status of College Students

College students psychological well-being is an important determinant of their academic success, social behavior, and general well-being. Conventional measurement tools for assessing mental health, including self-assessment questionnaires and clinical psychological tests, tend to be subjective, time-consuming, and inconstant, with the risk of misdiagnosis or delayed detection. Current machine learning and deep learning architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) methods employed for mental health detection. These, however, may not easily cope with the complex, multimodal psychological indicators and are non-adaptive to behavioral variations across individuals. The proposed model is integrated to process multimodal information, such as speech patterns, facial expressions, and behavioral measures. Through high-level feature extraction, sentiment analysis, and predictive modeling, the system has high accuracy in identifying stress, anxiety, and depression levels. The proposed method outperforms standard psychological assessment models with 96% classification accuracy, according to experimental results on real student datasets. This work introduces an intelligent and scalable solution towards early psychological health detection that allows for timely intervention and individualized support for students.