Entity set expansion (ESE) refers to getting a more complete set according to some rules, given several seed entities with specific semantic meaning. As a popular data mining task, ESE has many applications, such as dictionary construction and query suggestion. Contemporary ESE mainly utilizes text or Web information. That is, the intrinsic relations among entities are inferred from theirco-occurrences in text or Web. With the surge of knowledge graph in recent years, it is possible to extend entities according to their co-occurrences in knowledge graph. This paper studies the problem of the entity set expansion in knowledge graph. That is, given several seed entities, how to obtain more entities by leveraging knowledge graph. Firstly, the knowledge graph is modeled as a heterogeneous information network (HIN), which contains multiple types of entities or relationships. Next, a novel method of entity set expansion based on frequent pattern under Meta path, called FPMP_ESE, is proposed. FPMP_ESE employs Meta paths to capture the implicit common traits of seed entities. In order to find the important Meta paths between entities, an automatic Meta path generation method is designed based on frequent pattern called FPMPG. Then, two kinds of heuristic and PU learning methods are developed to distribute the weights of Meta paths. Finally, experiments on real dataset Yago demonstrate that the proposed method has better effectiveness and higher efficiency compared to other methods.