With the advent of big data era, the rapid growth of multi-source heterogeneous data has become an open problem. The inherent relationships between these data are usually modeled by the graph model. However, in practical applications, such as network security analysis and public opinion analysis over social networks, the structure and content of the graph data describing relationships between entity objects are usually not fixed. To be specific, the structure of the graph data, and the attributes of the nodes and edges in it will vary over time. Therefore, efficient query and match over dynamically updated graph data currently draws extensive research, where many outstanding research works are proposed. In this paper, the research progress of dynamic graph data matching technologies is reviewed from the aspects of key technologies, representative algorithms and performance evaluation. The state-of-the-art applications, the challenging problems and the research trend of dynamic graph matching technologies are summarized.