Abstract: It is of great significance for academic researches to search out the most influential papers from a huge number of Journal papers. However, the existing algorithms for maximizing influence need to be combined with greedy algorithm, which increases the time complexity. According to the time unidirectional and acyclic features of the citation relationship in the citation network, an algorithm is proposed to maximize the influence based on the global influence of nodes. The algorithm mainly includes: ①Calculating the global influence of all nodes. Combined with the publication time characteristics of the citation network, the upper triangular sparse influence matrix is constructed. On the basis of the linear threshold propagation model, the direct and indirect path effects between nodes and the cumulative calculation rule are used to simulate the propagation process of influence on the network. Every time the square matrix is calculated, the influence of all nodes will be propagated down one hop to get the influence of the next path, and all the influences will be counted to finally get the square matrix representing the global influence of all nodes; ②All nodes will be ranked according to the global influence, and the first n nodes will be selected as candidate nodes to select k seed nodes. By the cumulative calculation rule, the proposed algorithm avoids the overlapping of influence among nodes during the process of selecting seed nodes. The real academic citation network data set is taken as the experimental sample, and our algorithm is compared with the two benchmark algorithms in terms of activation range and running time. Experimental results show that the proposed algorithm greatly reduces the time complexity, and that the activation range is close to the greedy algorithm.