Node importance evaluationis an important direction of social network research. Ranking influential nodes in social networks is of great theoretical and practical significance to further identify the most influential nodes and understand the network structure. Anode importance evaluation algorithmis proposed in this paper, which combines local (degree centrality) and global (positioninformation) information with entropy method. We calculate the distance between nodes and the local core node set as position information. Then, wetake into account the influence of the node itself and that of neighbor nodes as the final importance of the node. The results show that the proposed method is effective in comparison experiment in three aspects: monotonicity, correctness and efficiency.