Abstract: For parameter optimization of support vector machine in fault diagnosis method of rolling bearing based on support vector machine, an improved fruit fly optimization algorithm was proposed which took accuracy rate of pattern classification as taste concentration function of fruit fly. The improved algorithm was used to optimize penalty factor and kernel function parameter of support vector machine model. Classified diagnosis of fault patterns of rolling bearing was maken based on the improved fruit fly optimization algorithm and support vector machine. The experimental results show that the improved fruit fly optimization algorithm has higher convergence speed and the optimization efficiency, and fault diagnosis method of rolling bearing based on the improved algorithm and support vector machine has higher classification accuracy rate.