As a crucial component of navigation equipment, bearings affect the positioning accuracy and safeguarding capability of the navigation equipment. In predicting the remaining useful life (RUL) of equipment, traditional machine learning algorithms are limited to dealing with the problems of complex nonlinear characteristic signals. For the above-mentioned reasons, a new prediction framework for RUL of bearing based on attention mechanism(AM) and bidirectional long short-term memory (Bi-LSTM) is proposed (Bi-LSTM-A). First, a one-dimensional convolution neural network (CNN) is added to the front of the structure to extract local features from the original signal sequence, and then, the signals are analyzed and predicted by combining bidirectional long short-term memory network with attention mechanism. finally, the predicted results are output through the fully connected layers at the end of the network. In comparison with the similar algorithms, the results show that the proposed method can accurately predict the equipment remaining useful life, and is good in predicting efficiency and accuracy.