Aiming at the problem that the classification accuracy of nonlinear support vector machine is susceptible to kernel function, a multi-scale kernel support vector machine (MSK-SVM) classification model is proposed and applied to rolling bearing fault diagnosis. In this model, Morlet, Marr and DOG wavelet kernel functions are introduced on the basis of Polynomial, Gaussian and Sigmoid kernels. Using the global and local characteristics of various kernel functions, as well as the characteristics that kernel functions with different scale parameters have distinct influence range, kernel functions with different characteristics and scale parameters are combined as multiscale kernel. Based on the gradient descent method, the weights of multi-scale kernel function are adaptively determined, and the MSK-SVM rolling bearing fault diagnosis models are obtained. In order to illustrate the effectiveness of the algorithm, the rolling bearing fault data set and life cycle data set are selected for experimental verification, respectively. The classification performance of MSKSVM models based on different characteristic kernel functions and the same characteristic kernel function are analyzed. The results show that the proposed algorithm can achieve higher classification accuracy and better generalization ability than the traditional single kernel SVM.