Bearings are one of the important components in the rotating machinery; it is significant to assess its performance degradation and predict the remaining useful life of the bearings by using sensors data to improve the reliability and decrease the maintenance costs. For the traditional datadriven approaches that rely on prior knowledge or expert experience in feature extraction and do not fully model middle and longterm dependencies hidden in timeseries data, we propose an endtoend deep framework for bearing performance degradation prognosis based on convolutional neural network（CNN）and bidirectional long shortterm memory（BLSTM）. The model adopts threelayer structure, the neural network firstly uses CNN to extract feature vectors directly from raw sensor data, then, the feature vector is constructed in a time series manner, and the BLSTM layer is introduced to capture temporal feature, finally, fullyconnected layers and the linear regression layer are built on top of BLSTM to predict the target value. The results of bearing accelerated life experiments show that the RMSE and MAPE of the proposed method are 127% and 171% lower than the traditional methods, indicating that the method can effectively improve the prediction accuracy of bearing performance degradation.