With the development of smart power grid, aSquantitySof monitoring devices are deployed to form sensor networks, resulting in a large amount of heterogeneous data. Traditional data management methods are difficult to effectively utilize the massive data. Because of the strong knowledge representation and reasoning ability, power knowledge graph can comprehensively and systematically display the business information, technical knowledge, industry standards and the internal relationship of these information. In this paper, a knowledge graph model of power grid scheduling rules is constructed by graph convolutional network (GCN), and the features of the knowledge graph are extracted based on graph neural network (GNN).SSpecifically, we obtain the entity words and relation words in the specifications from the smart grid dispatching control system with the knowledge extraction method, so as to clarify the object entity set and eliminate data ambiguity. Then, get the relationship between entities to construct domain knowledge graph, and normalize the object relation network of power intelligent dispatching. Finally, we learn the characteristics of the grid dispatching knowledge graph and mine the hidden relations between entities with the graph neural network, which can provide a comprehensive decision basis for the development of the grid dispatching system technology. The technical specification of smart grid dispatching control system involved in this paper is provided by The Northeast Branch of State Grid, and the method proposed in this paper is verified on the real data set.