Data-driven methods have been widely applied to fault diagnosis and aging predictions to assist fuel cell Prognostic and Health Management (PHM) system, in order to achieve early maintenance management and corrective measures for fuel cell systems. This paper proposes a novel fuel cell aging prediction method considering the applicability of data and algorithm. This method first adopts empirical mode decomposition (EMD) to split the aging data into several intrinsic mode functions (IMFs), and each IMF represents a different characteristic. Then the sample entropy (SE) is used as the quantitative criterion for complexity threshold. Furthermore, the nonlinear autoregressive neural network (NARNN) and the Long Short-Term Memory (LSTM) recurrent neural network are combined to ensure the applicability of data and algorithm. The results show that EMD can split the various data types of the aging data and weaken or even eliminate the excessive mutation phenomenon that occurs at the beginning of each experimental fuel cell. In addition, the targeted selection of data-driven methods can ensure the applicability of the data and algorithm. Finally, by comparing different prediction methods, the proposed method shows higher accuracy in the prediction of each experimental dataset, and good generality for different fuel cell types. This work was funded by the: National Natural Science Foundation of China , Grant numbers: 51977177 ; Shaanxi Province Key Research and Development Projects , Grant numbers: 2022QCY-LL-11 , 2021ZDLGY11-04 ; Fundamental Research Funds for the Central Universities , Grant numbers: D5000230128 ; Natural Science Basic Research Program of Shaanxi Province , Grant numbers: 2020JQ-152 .