Abstract: How to efficiently measure the importance of nodes has been a hot issue in the research of complex networks. In the research of node importance, many algorithms have been proposed to judge key nodes, but most of them are limited to high time complexity or single evaluation angle. Considering that entropy can be used to quantitatively describe the amount of information, this paper proposes a node importance ranking algorithm based on cross entropy. This algorithm takes into account the overall influence among the central node and its neighbor nodes, organically fuses the neighborhood topology information of nodes, and uses cross entropy to quantify the information differences between nodes. In order to verify the performance of the algorithm, this paper first uses monotone relation, maximum connectivity coefficient, network efficiency and SIR model as evaluation indicators, and then compares with other seven algorithms on eight real networks in different fields. The experimental results show that the algorithm proposed in this paper is effective and applicable, and the time complexity is only O(n) , which is suitable for large networks.