Depression is a common psychological disorder. As the pace of life in modern society accelerates, competitive pressure continues to increase, and the incidence of the disease is on the rise. With the development of brain network (Brain network is a term in medical imaging technology) technology research, many scholars have applied it in the computer-aided diagnosis of depression. At present, a large number of classification studies based on the brain network data of depression are mostly based on the brain network at a single spatial scale, and the features used are mostly clinical indicators or basic building elements of the brain network. The manifestations of depressive episodes can be divided into core symptom groups, psychological symptom groups and somatic symptom groups, but these typical symptoms do not necessarily appear in all patients. Some studies focus on comparing feature selection methods and selecting features to derive the optimal solution for assisting diagnosis of depression, significantly improving the overall performance of the system. The task of this work is to converge neural network (CNN) models and long and short-term memory (LSTM) models, where LSTM is used to simulate the environment. The environment attribute vector comes from verbatim repetition and is extracted verbatim using the CNN model. Features are automatically found in the vector sequence. The sum feature will be retrieved locally, and then the local feature will be combined with the global feature to enhance the result. Finally, it was found that the total score of social support in the depression group was (34.84±7.66) points, and the control group was (35.62±3.19) points. The experimental results show that the social support obtained by depression patients is significantly different from that of the control group, but the utilization of social support is significantly lower.

He Yu. Depression Classification Based on Deep Learning Sentiment Classification Model. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 9: 51-59. https://doi.org/10.25236/AJCIS.2024.070907.

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