Aiming at the problem that rolling bearing fault signals are easily disturbed by environmental noise and it is relatively difficult to obtain fault feature information, a rolling bearing fault feature extraction method based on VMD and fast spectral kurtosis is proposed. First, the bearing signal is decomposed into several IMF components, and then the maximum correlation kurtosis deconvolution algorithm is used to calculate the modal components of each order, and several IMF components with relatively large correlation kurtosis values are selected as the most prominent study of the fault information object and perform fast spectral kurtosis analysis on it; finally, set the filter frequency range according to the results of the fast spectral kurtosis map, and perform square envelope spectrum analysis on the filtered signal to obtain the fault characteristic information of the bearing. Public data and experimental analysis show that this method can successfully diagnose bearing faults.