Abstract: In order to solve the problem of reducing the precision of node identification between layers and within layers due to the hierarchical classification of complex networks by the classical K-shell decomposition algorithm, the network cross-layer adjacency entropy (multi-layer iterative information entropy) algorithm was proposed. Firstly, the decomposition process of K-shell decomposition algorithm was improved, and cross-layer centrality and cross-layer center degree of network were proposed to refine the importance of network node location. Secondly, the node location information and neighbor information contained in the network cross-layer centrality, neighborhood centrality and information entropy were analyzed comprehensively, and the network cross-layer adjacency entropy algorithm was proposed to identify the importance of network nodes. Finally, five kinds of networks with different topologies were compared with other algorithms in terms of monotonicity, accuracy and time performance. The experimental results show that the monotonicity of the cross-layer adjacency entropy algorithm is up to 0.9999, and the accuracy is up to 21% higher than other algorithms, which indicates that the proposed algorithm has better ability to identify network nodes.