关键词: 中文文本分类, k最邻近, 中心文档, 语义相似度, 聚类

Abstract: In order to search or extract information in a special category from large data source，text automatic categorization has become a hot subject of research.KNN is an important method of text automatic categorization，it can deal with large data sets with more stability，but it faces with the problem of slow speed.Based on KNN classification，the semantic relation of feature items is introduced，and clustering to build center documents under it.This method reduces the number of documents which KNN should search，and increases the speed of classification.Simulation results show that the proposed algorithm improves the speed in the case of traditional classification precision.

Key words: Chinese text classification, k-Nearest Neighbor（KNN）, center documents, semantic similarity, clustering