Poststroke depression (PSD) is one of the common complications after stroke, which seriously affects the rehabilitation of stroke patients. At present, the diagnosis of PSD is mainly based on the clinical manifestations of patients with various scales, but this method has certain subjectivity. Electroencephalography (EEG) combined with deep learning techniques has the potential to provide objective criteria for the diagnosis of PSD. In this study, we collected EEG signals from 28 subjects without poststroke depression (PSND) and 38 subjects with poststroke mild depression (PSMD), and proposed an end-to-end PSD diagnostic framework, which combines long short-term memory (LSTM) based on attention mechanism with convolutional neural network (EEGNet). LSTM model is used to learn the time-series dependencies of the EEG signal. attention mechanism assigns weights to the time domain information to improve the utilization of useful information. Finally, EEGNet module is used to extract more representative deep features in the EEG signal. The results showed that the accuracy, precision, recall, F1-Score and Kappa coefficient obtained by 10-fold cross-validation were 95.90%, 95.75%, 96%, 95.82% and 91.60%. Compared with the basic deep learning model for EEG-based PSD classification, our method maintains stable model performance and has high accuracy for the diagnosis of PSD, which provides a certain reference for the screening and diagnosis of PSD.