Aiming at the problem of motor broken rotor bar fault diagnosis, a fault diagnosis model is designed. This paper transforms the rotor broken bar fault diagnosis problem into a classification problem, thus a fault diagnosis model based on multiple hyper-spheres support vector machine (MHSVM) is proposed. MHSVM is a multi-class classification model, which is constructed by using support vector data description (SVDD) and binary tree structure. To verify the effectiveness of the proposed algorithm, MHSVM is compared with support vector machine (SVM) and neural network algorithm (BP). The results show that the diagnosis rate of MHSVM is 94.92%, while the diagnosis rate of SVM model and BP model is 92.06% and 89.06% respectively. As can be seen, the diagnosis rate of the proposed model is the highest among the three models. The experimental results show that the effect of the fault diagnosis models based on SVMs is better than that of BP. Meanwhile, MHSVM has the best effect for broken rotor bar fault diagnosis, which proves the applicability and effectiveness of the MHSVM.