In the knowledge base there exist characteristics of sparse for a single entity, and it is difficult to determine the similarity threshold of clustering. Therefore, this paper presents a name disambiguation algorithm based on cluster by step. Firstly, query features for character attribute are obtained from knowledge base, and the initial clustering based on knowledge base is carried out by text retrieval, which make up characteristics of sparse for a single entity name defined in knowledge base. Then, taking initial clustering results as input, name disambiguation in knowledge base is completed by using hierarchical clustering algorithm based on adaptive threshold. Finally, the other classes are identified by conditional random fields, and the cluster by using hierarchical clustering algorithm based on adaptive threshold is completed. The experiment on data of CLP2012 Chinese person name disambiguation results shows that the proposed algorithm can effectively achieve disambiguation names.