[Abstract] Data may be sparse in clustering process. If the traditional Euclidean distance is still used as clustering index, the quality and efficiency of clustering will be seriously affected. Inspired by KL divergence in information theory, similarity measurement method based on KL divergence is used in this paper to describe the overall dis-tribution of data, and then cluster the data. The information provided by the elements can be used in the cluster to measure the relationship between different clusters, overcome the shortcomings of traditional Euclidean distance and improve the accuracy of the algorithm. Finally, an experiment is carried out to verify the effectiveness of the algorithm.