Abstract: With the explosive growth of Web information in Internet，it seems that the current search engines cannot meet the requirement of users in many aspects.By grouping similar Web documents into clusters，the search space can be reduced，the search accelerated，and its precision improved.An integrated clustering algorithm for Web document is proposed in this paper，which combines SOM to realize coarse clustering and the improved PSO to realize fine clustering.Firstly，the Web document is expressed as feature lemma and its weight by the vector space model.Secondly，the SOM algorithm is used to realize coarse clustering of the document feature set and a group of output weights can be obtained.Then the improved PSO algorithm is initialized with the output weights and fine clustering can be realized by the algorithm evolution，thus Web document clustering is implemented finally.Simulation result shows that the algorithm can greatly improve the precision and recall of document searching，and have certain practical value.