In this paper,a support vector machine-based multi-model predictive control is proposed,in which SVM classification combines well with SVM regression. At first,each working environment is modeled by SVM regression and the support vector machine network-based model predictive control (SVMN-MPC) algorithm corresponding to each environment is developed,and then a multi-class SVM model is established to recognize multiple operating conditions. As for control,the current environment is identified by the multi-class SVM model and then the corresponding SVMN-MPC controller is activated at each sampling instant. The proposed modeling,switching and controller design is demonstrated in simulation results. In this paper, a support vector machine-based multi-model predictive control is proposed, in which SVM classification combines well with SVM regression. At first, each working environment is modeled by SVM regression and the support vector machine network-based model predictive control (SVMN-MPC) algorithm corresponding to each environment is developed, and then a multi-class SVM model is established to recognize multiple operating conditions. As for control, the current environment is identified by the multi-class SVM model and then the corresponding SVMN -MPC controller is activated at each sampling instant. The proposed modeling, switching and controller design is demonstrated in simulation results.