In proton exchange membrane fuel cell (PEMFC) life prediction, the unknown degree of influence of the characteristics in the fuel cell on its life makes the problem of predicting the remaining life of the fuel cell relatively complex. In order to more accurately predict the remaining service life of the fuel cell. In this paper, the original stack voltage was de-noised by wavelet analysis to filter the noisy data. pearson correlation coefficient (PCC) was used to reduce the dimension of influencing factors, extract key influencing factors, and simplify the model structure. Then, the improved sparrow search algorithm (ISSA) is used to optimize the BP neural network, find the optimal weights and thresholds of the network, and establish the ISSA-BP model. Finally, the processed data is input into the ISSA-BP model to predict the remaining life of PEMFC.The experimental results show that the average absolute error percentage, average absolute error, and root mean square error of PCC-ISSA-BP are 0.125%, 0.003 97, and 0.005 68, respectively, which are better than other models and can more effectively predict the remaining life of fuel cells.