Aiming at the problem of fault diagnosis caused by noise pollution and the vague of fault characteristic frequency. A new method for fault diagnosis of rolling bearings is proposed. Firstly, the Gini Index (GI) is used to evaluate the health status of rolling bearings, and the vibration signal with abnormal state is used for noise reduction preprocessing using the optimal parameter Maximum Correlated Kurtosis Deconvolution (MCKD) to highlight impact component.Then,calculate the hierarchical entropy (HE)of the preprocessed signal to form a feature matrix.Finally, the cuckoo search algorithm is used to optimize the relevant parameters of the support vector machine, and the intelligent diagnosis of the fault state of the rolling bearing is completed.The feasibility of the proposed method is verified by experimental analysis, and it has a high accuracy.