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Fault diagnosis of rolling bearings is of great importance to improve the efficiency of industrial production and ensure the stable and safe operation of industrial production.In order to improve the accuracy of rolling bearing fault recognition, a fault diagnosis method based on KNN - naive Bayesian decision combination algorithm is proposed. The combined algorithm uses the naive Bayesian algorithm to reclassify the KNN algorithm using different K values to achieve the purpose of improving the fault identification of rolling bearings. Firstly, the energy feature extraction of the rolling bearing vibration signal is carried out by using the wavelet packet energy method. Then, the KNN algorithm is used to pre-classify the energy characteristic data, and the KNN algorithm is used to classify the result result group. The classification result set is used as the input of the naive Bayesian algorithm, and the data is reclassified using the naive Bayesian algorithm. The experimental results show that the combined algorithm is effective compared with the traditional KNN algorithm and the Bayesian algorithm in the fault diagnosis of rolling bearings,The combination algorithm realizes the effective diagnosis of rolling bearing faults.