The bearing fault signatures are difficult to be extracted effectively under strong background noises. To address this issue, this article proposes a rolling bearing fault diagnosis method based on the parameter adaptive feature mode decomposition. Firstly, to overcome the shortcoming that the original characteristic mode decomposition (FMD) needs to rely on human experience to set its key parameters without adaptability, the grid search method based on feature energy ratio of squared envelope spectrum ( FER-SES) is presented to automatically determine the mode number n and the filter length L of FMD. Then, the original bearing vibration signals are divided into n mode components by parameter optimized FMD. The mode component with the maximum FER-SES is selected as the sensitive mode component. Finally, the fault characteristic frequency is extracted by calculating the squared envelope spectrum of sensitive mode component to distinguish bearing fault types. The effectiveness of the proposed method is evaluated by simulation signal and engineering case analysis. Compared with variational mode decomposition and spectral kurtosis, the proposed method has better fault feature extraction performance.