Most of existing web page clustering algorithms are based on short and uneven snippets of web pages, which often cause bad clustering performance. This paper presents a clustering algorithm based on frequent itemsets meaning sequence, which combines the use of WordNet syntactic and semantic features to build the search results clustering and labeling. Most of existing text clustering algorithms use the vector space model, which treats documents as bags of words. A word（meaning）sequence is frequent if it occurs in more than certain percentage of the documents in the text database. Firstly, the text is pre-processed to generate compact document to reduce the dimension of the document, build generalized suffix tree, and dig out the maximum frequent itemsets, then the frequent word meaning sequences is generated. Document theme can be better reflected by frequent itemsets meaning sequence, the search results having same themes clustered together with the user's query prioritization highly relevant. Experimental results show that the clustering algorithm can obtain a high quality cluster that related to the query semantic tags, which has higher accuracy, efficiency and good scalability.