Aiming at the problem that the influence of PEMFC characteristics on the life prediction method of the proton exchange membrane fuel cell (PEMFC) is unknown and the low prediction accuracy of the model, a PEMFC remaining life prediction method based on XGBoost-RFECV algorithm and LSTM neural network is proposed. First of all, the PEMFC original data is reconstructed and smoothed by equal interval sampling and SG convolution smoothing method, which effectively retains the original data degradation trend. Then the XGBoost-RFECV algorithm is used to calculate the importance of different PEMFC features, and the 10 PEMFC features with the smallest mean square error of average cross-validation are selected to form the optimal feature subset. Finally, the optimal feature subset is input into the constructed two-layer LSTM neural network to realize the remaining life prediction of PEMFC. The experimental results show that the average absolute error and root mean square error of the method are 0. 001 9 and 0. 002 5, respectively, and the coefficient of determination R 2 is 0. 974. Compared with the XGBoost-RNN, XGBoost-LSTM and XGBoost-RFECV-RNN model, the prediction accuracy is higher and it can effectively predict the remaining life of PEMFC.