Most of the real-life networks are heterogeneous networks that contain multiple types of nodes and edges, and heterogeneous networks integrate more information and contain richer semantic information than homogeneous networks. Heterogeneous network representation learning to have powerful modeling capabilities, enables to solve the heterogeneity of heterogeneous networks effectively, and to embed the rich structure information and semantic information of heterogeneous networks into low-dimensional node representations to facilitate downstream task applications. Through sorting out and classifying the current heterogeneous network representation learning methods at home and abroad, reviewed the current research status of heterogeneous network representation learning methods, compared the characteristics of each category model , introduced the related applications of heterogeneous network representation learning, and summarized and prospected the development trend of heterogeneous network representation learning methods. It is proposed that in-depth discussion can be carried out in the following aspects in future: First, avoid predefined meta-paths and fully release the automatic learning capabilities of the model; Second, design heterogeneous network representation learning method suitable for dynamic and large-scale networks.