Abstract: With the rapid development of the Internet, the number of pages is growing explosively. This presents a huge challenge for search engines which provide Web page search services. There are also lots of similar or even the exact same content pages and low-quality pages. In term of search engine, indexing such pages is no significant effect for retrieval results, but increases the search engine indexing and retrieval burden. A page selection algorithm is proposed to build indexing page collection from massive Web data for search engine. One hand, signature-based cluster algorithm is used to filter the similar pages to compress the size of the indexing page collection; on the other hand it combines a variety of features of the page dimensions and user dimensions, to ensure the quality of the collection. This algorithm is not only able to quickly cluster and select pages, but also achieve a higher compression ratio while still preserving the amount of information present in the indexing page collection. Experiments with actual page collections show that the size of indexing page collection selected by the proposed algorithm is about the entire page collection by 1/3, and can meet the vast majority of user click needs, with a strong practical.