In order to extract features of sleep EEG more comprehensively,a sleep EEG staging method based on multi- view and attention mechanism is proposed.First,two types of view data,namely time domain and time-frequency domain,are constructed based on the original sleep EEG signal.Then,a hybrid neural network with attention mechanism is designed to perform representation learning on multi-view data.Next,the transition rules between sleep stages are further learned through a bidirectional long short-term memory network.Finally,the Softmax function is used for sleep staging,and the class weighted loss function is utilized to solve the problem of unbalanced sleep data categories. In this experiment,the single-channel EEG signals of the first 20 subjects in the Sleep-EDF database are used,and 20- fold cross-validation is adopted to evaluate the performance of the model.The accuracy of sleep staging reaches 83.7%, the macro-F₁-score(MF₁)reaches 79.0%,and the Cohen's Kappa coefficient reaches 0.78.Compared with the existing methods,the performance of the algorithm in this paper is significantly improved,which proves the effectiveness of the proposed method