Accurate identification of influential nodes in complex networks is crucial for network management and net-work security.The local centrality method is concise and easy to use,but ignores the topological relationship between neighboring nodes and cannot provide globally optimal results.A node importance assessment method was proposed to correlate the node edge relationship and topology,which firstly applied the H-index and information entropy to assess the nodes,then combined the structural holes of the nodes with the structural characteristics of the nodes,and took into ac-count the attribute of"bridging"while focusing on the node's own quality and the amount of information about the neigh-boring nodes.The algorithm was validated by simulating the propagation process using the SIR model,and the Kendall correlation coefficient,complementary cumulative distribution function and propagation influence were applied to vali-date the validity and applicability of the method.Comparison of the experimental results on six real network datasets shows that the proposed method is more accurate than the traditional centrality methods in identifying and ordering the key nodes in the network.
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