Heterogeneous network similarity learning is to analyze the degree of correlation between two different types of objects. Different types of objects have different degrees of importance in heterogeneous networks

and play different roles in the similarity learning process.This paper proposes a node influence based similarity measure method (NISim) heterogeneous information network. This method not only considers the link structure in network but also keeps the semantic information in heterogeneous networks. Also

this method distinguishes the effect to heterogeneous network brought by different types of nodes. In heterogeneous network

the heuristic rules are used to distinguish and quantify the influence weight of different types of nodes. In addition

the link structure in network and the semantic relationship are combined to solve the problem of improving similarity learning accuracy. Experimental results show that this method can measure the similarity between different types of nodes effectively. It can be applied in different fields such as network search

recommendation system and knowledge graph construction and so on.