Literature automatic categorization of Chinese academic journals based on the manual labeling

A new literature categorization method based on the manual labeling in Chinese academic journals is introduced to solve the text categorization problem for electronic journal data processing. In this method, the term vector space of text is described by automatic word segmentation. A categorization rule integrates both the term frequency and the inverse document frequency weights by considering the key effect of the manual labeling. The class expert database is built through sample training and the similarity between the known class and the text to be categorized can be computed to determine the text class. Experiments show that the recognition rate of this method is about 85%.