This paper proposes a support vector machine-based fuzzy rules acquisition system(SVM-FRAS) .The character of SVM in extracting support vector provides a mechanism to extract fuzzy If-Then rules from the training data set.We construct the fuzzy inference system using fuzzy basis function(FBF) .The gradient technique is used to tune the fuzzy rules and the inference system.Theoretical analysis and comparative tests are performed comparing with other fuzzy systems.Experimental results show the SVM-FRAS model possesses good generalization capability as well as high comprehensibility. This paper proposes a support vector machine-based fuzzy rules acquisition system (SVM-FRAS). The character of SVM in extracting support vector provides a mechanism to extract fuzzy If-Then rules from the training data set.We construct the fuzzy inference system using The gradient technique is used to tune the fuzzy rules and the inference system. Theoretical analysis and comparative tests are compared with other fuzzy systems. Experimental results show the SVM-FRAS model possesses good generalization capability as well as high comprehensibility