To improve the accuracy of bearing fault diagnosis, a support vector machine decision tree multi-fault classifier, which extracts attribute characteristics by using grey correlation Information entropy, is proposed based on grey correlation theory and information entropy theory. The classifier achieves the classification of multiple fault types of bearing, verifying various faults of bearing. The result shows that the method can recognize the fault condition effectively, achieving the purpose of the accurate multi fault diagnosis of mechanical systems.