To improve the accuracy of automatic recognition of depression, a fusion deep residual network is proposed as the encoder, and an improved long short-term memory network and self attention mechanism are introduced to enhance the network model’s perception ability of data temporality, resulting in a well performing PLSTM AttnNet model. Firstly, preprocessing operations such as filtering and denoising are performed on the EEG signal to extract six typical time-domain data and differential entropy and power spectral density covering five frequency bands: δ, θ, α, β, and γ. Input the preprocessed data into the ResNet encoder to extract spatial dimension information, use the PLSTM module and self attention module to process the output features of the ResNet encoder to capture the temporal dimension information of the data, and improve the recognition accuracy of depression. A large number of comparative experiments were conducted on the publicly available EEG dataset, and the quantitative results showed that the proposed PLSTM AttnNet model achieved a classification accuracy of 97.3%. Compared with the single ResNet model and ResNet PLSTM model, the accuracy was improved by 10.1% and 3.9% respectively, and it was significantly better than the comparative model in evaluation indicators such as F1 score. The results indicate that the innovative fusion of ResNet and PLSTM Attention effectively enhances the collaborative representation ability of EEG signal temporal spatial features, solves the problem of one-sided feature capture in traditional models, and provides a reliable solution for efficient automatic recognition of depression, which has important clinical application value.