In relation extraction tasks, building dependency trees or syntactic trees is usually adopted to obtain deeper and richer structural information. Graph neural network, as a powerful representation learning method for graph data structures, can better model such complex data structures. This study introduces a relation extraction method based on graph neural network, aiming to gain a deep understanding of the latest research progress and trends in this field. Firstly, it briefly introduces the classification and structure of relation graph neural networks and then elaborates on the core technology and application scenarios of relation extraction methods based on graph neural networks, including sentence-level and document-level methods, and joint entity-relation extraction methods. The advantages, disadvantages, and performance of each method are analyzed and compared, and possible future research directions and challenges are discussed.