The purpose of influence maximization is to find a small group of nodes in a network that can trigger the maximum number of remaining nodes to participate in the process of information transmission. At present, the research on the influence maximization of heterogeneous information networks usually extracts homogeneous subgraphs from the network or evaluates the influence of nodes according to the meta-path of local node structure. However, it does not consider the global features of nodes and the influence loss of the final spread range of the seed set caused by the clustering phenomenon among highly influential nodes. This study proposes an influence maximization algorithm for heterogeneous information networks based on community and structure entropy, which can effectively measure the influence of nodes locally and globally. Firstly, the local structure information and heterogeneous information of nodes in the network are retained by the construction of meta-structure to measure the local influence of nodes. Secondly, the global influence of nodes is measured by the weight ratio of the community to which the nodes belong to the whole network. Finally, the final influence of nodes is calculated, and the seed set is selected. Many experiments on real data sets indicate that the proposed algorithm is effective and efficient.