Traditional methods for remaining useful life（RUL）prediction of mechanical equipment require manual intervention processes such as multisource data fusion and establishment of health indicators, and the prediction accuracy is limited by the ability of health indicators to characterize the degradation process of the equipment. To achieve endtoend RUL prediction and improve the prediction accuracy, a RUL method based on a combination of attention mechanism and residual depth separation convolutional network is proposed, and the effectiveness of the method is tested by using the CMAPSS aeroengine simulation data set. A sliding window is used to intercept multivariate sequences from the engine multisource state parameters as samples to characterize the engine state, and a RUL prediction model is built based on a onedimensional separable convolutional network, and an attention mechanism and residual network are introduced into the network to improve the prediction accuracy of the model. The final mean root mean square error values of the proposed method for the four test sets of C-MAPSS are 11.28, 14.12, 11.57 and 15.61, respectively, and it also has good generalization capability for RUL prediction during engine operation. The comparison results with various RUL prediction methods show that the overall prediction accuracy of the proposed method is high for all four test sets, indicating that the method is an effective RUL prediction method for mechanical equipment and can be used for early fault warning of equipment.