College students are under increasing competition pressure, which has a negative impact on their mental health, as the pace of learning and life accelerates, as well as the increasingly difficult employment situation. As a result, emphasizing the importance of college students’ mental health and fully addressing it has become a top priority in the work of colleges and universities. However, some students and even teachers are currently unconcerned about mental illness, making it difficult for students with psychological abnormalities to receive timely detection and effective treatment. As a result, it is the responsibility of student management for colleges and universities to identify and intervene early in the mental health problems of college students. Through the use of multimodal data and neural network models, it is now possible to evaluate and predict the mental state of college students in real time, thanks to the advancement of intelligent technology. Therefore, a novel multimodal neural network model is proposed in this paper. Our model is divided into two branches in particular. The traditional mental health assessment and prediction algorithm, which is based on the improved BP neural network and the International Mental Health Scale SCL-90, is one of the branches. Given how difficult it is to meet the requirements for the accuracy of college students’ mental health assessments using this method, our other branch is computer vision-based facial emotion recognition of college students, which is used to aid in the evaluation of mental health assessments. Our model demonstrates competitive performance through simulation and comparative experiments.
MENTAL HEALTH ASSESSMENT OF SOCIAL NETWORK USERS BASED ON CONVOLUTIONAL NEURAL NETWORK
