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Due to the shortcomings of a single node evaluation method, under the premise of fusing the local and global features of complex networks, this paper proposed a node importance evaluation method based on overlapping box covering algorithm. This method used overlapping centrality to rank the importance of nodes in the network, and compared it with other different centrality methods in complex network data sets. In addition, it used the susceptible-infected(SI) model to simulate the propagation capabilities of the first 10 nodes of different centrality methods, and compared them with the Kendall coefficient on this basis. The larger the Kendall coefficient, the higher the correlation. The experimental results show that, compared with other centrality methods, the cumulative average infectivity ability of the initial node set obtained by overlapping centrality is higher than other centrality methods, and has a higher correlation with the SI model. This method is effective and feasible for the evaluation of node importance. [ABSTRACT FROM AUTHOR]