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. 2022 Jul 6:2022:7842304.
doi: 10.1155/2022/7842304. eCollection 2022.

Deep Learning-Based Mental Health Model on Primary and Secondary School Students' Quality Cultivation

Shuang Li  1 , Yu Liu  1
Affiliations

Deep Learning-Based Mental Health Model on Primary and Secondary School Students' Quality Cultivation

Shuang Li et al. Comput Intell Neurosci. .

Abstract

The purpose was to timely identify the mental disorders (MDs) of students receiving primary and secondary education (PSE) (PSE students) and improve their mental quality. Firstly, this work analyzes the research status of the mental health model (MHM) and the main contents of PSE student-oriented mental health quality cultivation under deep learning (DL). Secondly, an MHM is implemented based on big data technology (BDT) and the convolutional neural network (CNN). Simultaneously, the long short-term memory (LSTM) is introduced to optimize the proposed MHM. Finally, the performance of the MHM before and after optimization is evaluated, and the PSE student-oriented mental health quality training strategy based on the proposed MHM is offered. The results show that the accuracy curve is higher than the recall curve in all classification algorithms. The maximum recall rate is 0.58, and the minimum accuracy rate is 0.62. The decision tree (DT) algorithm has the best comprehensive performance among the five different classification algorithms, with accuracy of 0.68, recall rate of 0.58, and F1-measure of 0.69. Thus, the DT algorithm is selected as the classifier. The proposed MHM can identify 56% of students with MDs before optimization. After optimization, the accuracy is improved by 0.03. The recall rate is improved by 0.19, the F1-measure is improved by 0.05, and 75% of students with MDs can be identified. Diverse behavior data can improve the recognition effect of students' MDs. Meanwhile, from the 60th iteration, the mode accuracy and loss tend to be stable. By comparison, batch_size has little influence on the experimental results. The number of convolution kernels of the first convolution layer has little influence. The proposed MHM based on DL and CNN will indirectly improve the mental health quality of PSE students. The research provides a reference for cultivating the mental health quality of PSE students.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Connotation of mental health quality of PSE students.
Figure 2
Figure 2
Overall framework of the MHM.
Figure 3
Figure 3
Framework of CNN.
Figure 4
Figure 4
DeepPsy model.
Figure 5
Figure 5
Results of different classification models.
Figure 6
Figure 6
Comparison of model performance before and after optimization.
Figure 7
Figure 7
Comparison of results of different data features.
Figure 8
Figure 8
Robustness validation of the model.
Figure 9
Figure 9
Effect of the number of convolution kernels on the model.
Figure 10
Figure 10
Effect of batch_size value on the model.
Figure 11
Figure 11
PSE student-oriented quality cultivation strategy based on the proposed MHM.
See this image and copyright information in PMC

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