Abstract: In order to achieve accurate short-and long-term degradation prediction of fuel cells, a PEMFC degradation prediction model based on extreme gradient boosting（XGBoost） model was proposed. Firstly, the experimental data of fuel cell aging were processed to reduce noise and the voltage recovery characteristics were modeled by using double exponent. After, four multi-step ahead prediction model based on XGBoost and the long-term prediction strategy considering recoverability were constructed, and particle swarm optimization（PSO） algorithm was used to optimize the parameters of the model. Lastly, the prediction results of the four short-term prediction models were compared, and the optimal model was applied to the long-term aging prediction strategy. The results show that the XGBoost prediction model with multiple input multiple output（MIMO） strategy had the best prediction performance, which three-step ahead prediction’s root mean square error was 0.00465、mean absolute error was 0.00219 and operation time was 3.48 s. The average relative error of the remaining useful life（RUL） of the long-term prediction strategy based on MIMO-XGBoost and considering recovery was 7.74%, which was significantly better than the autoregressive integrated moving average method.