Aircraft brake pads play a very important role in the process of aircraft braking. It is of great significance to accurately predict the Remaining Useful Life (RUL) of aircraft brake pads for reducing braking faults and saving human and material resources. Aiming at the non-stationary and nonlinear characteristics of the aircraft brake pads wear sequence, a model for predicting the RUL of the aircraft brake pads based on Bidirectional Long Short-Term Memory (BiLSTM) network was proposed, namely VMD-BiLSTM model. Firstly, the method of Variational Mode Decomposition (VMD) was used to decompose the original wear sequence into several sub-sequences with different frequencies and bandwidths to reduce the non-stationarity of the sequence. Then, the BiLSTM neural network prediction models were constructed for the decomposed subsequences. Finally, the prediction values of the sub-sequences were superimposed to obtain the final prediction result of brake pads wear value, so as to realize the life prediction of the brake pads. The simulation results show that the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) of VMD-BiLSTM model are 0.466 and 0.898% respectively, both of which are better than those of the comparison models, verifying the superiority of VMD-BiLSTM model.