The goal of entity linking is to link entity mentions in the document to their corresponding entity in a knowledge base. The prevalent approaches can be divided into two categories: the similarity-based approaches and the graph-based collective approaches. Each of them has some pros and cons. The similarity-based approaches are good at distinguish entities from the semantic perspective, but usually suffer from the disadvantage of ignoring relationship between entities; while the graph-based approaches can make better use of the relation between entities, but usually suffer from bad discrimination on similar entities. In this work, we present a consistent collective entity linking algorithm that can take full advantage of the structured relationship between entities contained in the knowledge base, to improve the discrimination capability of the proposed algorithm on similar entities. We extensively evaluate the performance of our method on two public datasets, and the experimental results show that our method can be effective at promoting the precision and recall of the entity linking results. The overall performance of the proposed algorithm significantly outperform other state-of-the-art algorithms.