Analysis of Educational Mental Health and Emotion Based on Deep Learning and Computational Intelligence Optimization - Liu, Junli; Wang, Haoyuan (Frontiers in Psychology, 2022)
Understanding students' psychological pressure and bad emotional reaction can solve psychological problems as soon as possible and avoid affecting students' normal study life. With the improvement of global scientific and technological strength, and the step-by-step in-depth research on deep learning and computational intelligence optimization. Now, we have enough conditions to build a psychological and emotional data set for the field of education, and build a mental health stress detection model with emotional analysis function. In addition, a variety of experimental methods are used for comparison, which shows the superior performance of the model in practical application scenarios. The results show that: (1) the data set constructed for the model is reasonable. Psychological stress test shows that the tested college students are in good health and have no positive performance. Schools need to pay special attention to obsessive-compulsive disorder and interpersonal sensitivity, and the average values of both indicators are higher than 0.9. (2) For the optimization of ACO computational intelligence, both the stability and the average execution time of the algorithm are obviously higher than those of other algorithms. This model has obvious performance advantages after using this algorithm. (3) Using loss function value to measure the difference between simulated emotion analysis and real value. The difference of most emotion tests is less than 3%; the accuracy difference between sadness and fear is about 7%. Although the final results prove the feasibility of this method, there are still some shortcomings to be optimized.