Bearing as an indispensable part of mechanical parts, long-term work is easy to lead to bearing wear and fatigue failure, and then affect the normal operation of mechanical equipment. Therefore, the prediction of the remaining useful life (RUL) of the bearing can effectively avoid accidents and ensure the safe and reliable operation of the equipment. To enhance the prediction accuracy of the RUL for rolling bearings, this paper proposes a rolling bearing life prediction method based on spatio-temporal attention (STA) and bidirectional long short-term memory (BiLSTM). Effectively integrate multiple modal information in bearing data to capture changes in bearing operating state. Firstly, the original vibration signal is input to auto-encoder (AE) model to extract fault features automatically. Then, the extracted features are input into the STA model, and the spatial information and running time step information of the feature data in the feature dimension are deeply weighted to capture the information of the feature dimension and time dimension more comprehensively. Combined with BiLSTM model, the remaining service life of bearing is predicted. Finally, experimental validation is conducted using datasets from the PHM2012 Challenge and ABLT-1A bearing full-life cycle data.The experimental results indicate that the proposed model has achieved an average reduction of approximately 22.76% in RMSE and 26.57% in MAE, while the R2has improved by an average of about 12.47%. It can be concluded that the proposed method significantly enhances the accuracy of RUL prediction.