n order to decrease the sample training time effectively, improve the identification rate, and make the model has
good generalization ability, a new class partition project based on samples is proposed. This project makes a comprehensive
consideration of the number of waiting classification samples and the capability of class partition, and takes a compromise
between the“first classifying the classes with a large number of samples”and the“first classifying the classes that can be
partitioned easily”. And a new decision-tree-based support vector machines multi-class classification algorithm is proposed,
which adopts the balance decision tree structure. The experimental results show that the algorithm can significantly reduce
system training time at the condition of not reducing identification rate, and is an effective multi-class classification algorithm.