In order to improve the performance degradation prediction accuracy of proton exchange membrane fuel cell (PEMFC), a fusion prediction method (CKDG) based on adaptive noise complete ensemble empirical mode decomposition (CEEMDAN), kernel principal component analysis (KPCA) and dual attention mechanism gated recurrent unit neural network (DA-GRU) was proposed. CEEMDAN and KPCA were used to extract the input feature data sequence, reduce the influence of random factors, and capture essential feature components to reduce the model complexity. The DA-GRU network helps to learn the feature mapping relationship of data in long time series and predict the changing trend of performance degradation data more accurately. The actual aging experimental data verify the performance of the CKDG method. The results show that under the steady-state condition of 20% training data prediction, the CKDA method can reduce the root mean square error (RMSE) by 52.7% and 34.6%, respectively, compared with the traditional LSTM and GRU neural networks. Compared with the simple DA-GRU network, RMSE is reduced by 15%, and the degree of over-fitting is reduced, which has higher accuracy. It also shows excellent prediction performance under the dynamic condition data set and has good universality.