Abstract: Ranking is an essential part of information retrieval. Nowadays there are hundreds of features for constructing ranking functions and it is a hot research topic that how to use these features to construct more efficient ranking functions. So learning to rank, an interdisciplinary field of information retrieval and machine learning, has attracted increasing attention. Queries could be classified into several types based on different criterions, and the importance of ranking features is divergent for different types of queries. It is unpractical to apply a general ranking function for all queries. In this paper, we analyse the query features based on keyword mathcing and constrcut quey feautre vectors through the selected query features. Then the queries are clustered into several clusters and ranking functions are learned for each cluster. Finally, the fittest ranking function is chosen for a new coming query and ranks the documents. The experimental results show that the ranking functions based on query clustering with selected query features are comparable with or even outperfom the one based on all queries.