Abstract: There arc thousands of subjects in Chinese news subject specification. When they arc used in news classification,long training time and large model are two key problems we are facing, especially when some of classes are changed. Chinese news subject classification has hierarchical structure and hierarchical can solve the problem partially.We improved the Chinese news hierarchical classification to get better the result from two points of view. 1) Repetitious feature calculation represents news of different layers in hierarchical classification. 2) Use error control to solve the problem that one error classification in upper layer will lead in the error classification of its deeper classes. Our experimenu shows that hierarchical classification improves the precision of 4% comparing with flat classification, hierarchical classification with Repetitious feature calculation improves 3% comparing with hierarchical classification, and hierarchical classification with error control improves 3 % comparing with hierarchical classification.

Key words: Hierarchical classification,Support vector machine,Chinese news subject classification specification,Fcature calculation,Error control