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Combing one-versus-one decomposition strategy with support vector machine has become an efficient means for multi-label classification problem. But how to speed up its training and test procedures is still a challenging issue. In this paper, we generalize the primary binary support vector machine to construct a double label support vector machine through locating double label instances at marginal region between positive and negative instances, and then design a fast multi-label classification algorithm using the voting rule. Experiments on benchmark datasets Yeast and Scene illustrate that our novel method can be comparable with some existing methods according to some widely used evaluation criteria, and can run faster 17% averagely than the current corresponding method in training procedure.