The invention relates to a dynamic text cluster method based on network flow and laminated knowledge base, belonging to information processing and network safety technical field. The inventive method comprises that first assuming the clustered file type is provided with vector character, using TFIDF method to extract and normalize character of single clustered text, using the method that defining meaning distance in the knowledge base to calculate the distance of text and type, adjusting and refreshing keyword and weight of the type of new added file, when present file can not be combined with known types, needs to build new type. And the algorism comprises dynamic character vector extraction, type classification, distance calculation, type combination and new type construction. The invention is characterized in that the cluster process is based on the meaning information provided by the laminated knowledge base but not keyword, the invention can dynamically remove noise data, the similarity is calculated network flow algorism, to confirm optimized match, and the invention can meet real-time refresh cluster of Web text, in particular as non-detect type, without pre-appointing type group.