The study of influence maximization is an important theme in the research of social network information dissemination. Most of the previous research results are based on static social networks. Taking into account the temporal characteristics of real social networks, this paper focuses on the problem of influence maximization of temporal networks; that is, finding k nodes in the temporal network to maximize the spread of information. First, we define a method for calculating the propagation probability between nodes based on the eigenvector centrality. Secondly, we calculate the influence of nodes based on the local information and propagation probability of the nodes in the temporal network. Then, the independent cascade (IC) model is improved for the information propagation problem in the temporal network. On this basis, a temporal network influence maximization algorithm combining greedy and heuristics strategies (TCHG) is proposed. The algorithm selects the candidate seed node set in the heuristic stage, then selects the target seed node set from the candidate seed node set in the greedy stage. The experimental results show that compared with the existing heuristic algorithm, the TCHG algorithm has both a larger influence range and a reduced runtime, which reflects the accuracy and efficiency of the TCHG algorithm.