In order to solve the accuracy and complexity of multiple classification algorithms, on the basis of factor space theory, the divisibility measurement condition and the construction condition of binary tree are defined. On the basis of balanced binary tree, combining the relationship between class spacing and sample circle radius, a factor support vector multiple classification algorithm(M-FSV) is proposed by using recursion idea and the principle of “easy classification first”. Experiments are done with one-to-one support vector machine and balanced binary tree support vector machine algorithm, and experimental comparison is made. The results of 8 data sets in UCI database show that the training time of M-FSV is less than that of SVM, and the algorithm accuracy is higher than that of SVM. The research results expand the theory and application of factor space, and provide a new idea and simple method for classification problems in machine learning.