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JP+1]To improve the discrimination accuracy of conventional multi-class classification support vector data description (SVDD) methods, a multiple classification Support vector data description algorithm with Negative Samples is proposed. Based on the general model of SVDD, the proposed algorithm treats the samples in the class as the target class, while the other classes of overlap and sample is heterogeneous for the overlap region. By using SVDD algorithm with Negative samples, the hypersphere model is trained again until all hypersphere models optimized. Simulated experimental results show that the proposed algorithm can eliminate overlap and improve the discrimination accuracy. The algorithm is applied in the implemention of analog circuits fault diagnosis, comparing with SVDD classification algorithm, one-to-one and one-to-many SVM algorithm, results show that the algorithm is more effective and higher accuracy in fault diagnosis.