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According to issues on noise and isolated points sensitive, the traditional SVM can not solve the sample characteristics of large scale, containing the heterogeneous information and the uneven feature space distribution. The fuzzy membership is draw into multiple kernel learning, and this paper proposes a method of fuzzy multiple kernel learning. Experimental results show that fuzzy multiple kernel learning is more effective and feasible than SVM, Fuzzy support vector machine and Multiple kernel learning. So, proposed method in this paper can effectively overcome the traditional SVM on noise sensitive and data distribution uneven problem.