In order to realize the integrated diagnosis of multiple faults in the working process of electro-mechanical actuators (EMA), a fault diagnosis method of EMA based on dual-stage attention-based long short term memory (DaLSTM) combined model was proposed. Firstly, the multi-source sensor signal of the EMA is used as the input. The long short term memory (LSTM) neural network based on input attention and time attention is used to adaptively extract the relevant features in the original multi-source sensor data, and the time series prediction of multi-source sensors is realized by the DaLSTM combination model. Secondly, in the fault diagnosis time window, the minimum difference between the predicted value and the sampled value of the DaLSTM combination model under different states is used as the decision function to diagnose the fault type of EMA. Finally, time series prediction and fault diagnosis experiments are conducted using the public National Aeronautics and Space Administration (NASA) dataset, and the average recognition rate of fault categories reaches 98. 76%, which proves the effectiveness of the proposed method.