@article{3813,
  author = {Chu Yanhua},
  title = {Assessment of the Mental Health of Students Using Neural Networks},
  journal = {Journal of Networking Technology},
  year = {2023},
  volume = {14},
  number = {3},
  doi = {https://doi.org/10.6025/jnt/2023/14/3/68-75},
  url = {https://www.dline.info/jnt/fulltext/v14n3/jntv14n3_2.pdf},
  abstract = {With the increase of social pressure and mental health problems, evaluating and diagnosing college students’ mental health has become particularly important. To improve the accuracy and efficiency of assessment and diagnosis, we propose a neural network-based mental health model for college students. This model adopts deep learning technology to assess and diagnose students’ mental health by analyzing their personal information, behavioral data, and academic performance. Specifically, we used models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM) to conduct a multidimensional analysis of students’ learning behavior, emotional changes, and social situations. At the same time, we have also utilized advanced technologies such as self-attention and attention mechanisms to better capture students’ information from different dimensions. Through extensive experimental data validation, we found that the neural network-based mental health model for college students has high accuracy and efficiency. This model has higher diagnostic accuracy and a lower misdiagnosis rate than traditional evaluation and diagnostic methods. In addition, the model also has good generalization performance and can adapt to the application needs of different types of students and different scenarios.},
}