In today's rapidly developing information technology, massive amounts of data are generated every day. These data sources are complex, diverse, closely related, and large-scale. How to analyze and mine useful information from these data has become a challenge. As a basic data structure, graph nodes can depict real-world entities, and its edges can reflect the relevant relationships between entities. Therefore, graph based research has attracted a large number of scholars. Among the numerous research directions based on graphs, graph pattern matching has always been one of the most important research directions. We can design corresponding pattern graphs based on our own needs, and then use graph pattern matching technology to obtain matched subgraphs that meet the constraint conditions of the pattern graph. For example, we currently need to find a software development team that includes system architects, software development engineers, software testing engineers, project managers, and requirements analysts. In order to find a high-quality team, there are conditional constraints on every member of the team, such as age constraints, work experience constraints, and etc. In addition, it is also required that some members of the team get to know each other or indirectly, for example, in order to work together, software development engineers and software testing engineers must know each other or have common friends. At this point, each member in the team can be considered as a node, age constraints and work experience constraints can be considered as attribute constraints on the nodes, and the relationships between members can be considered as edges between nodes. In this way, the required pattern graph can be constructed according to the specific team requirements, and then match subgraphs that meet the constraint conditions of the pattern graph can be obtained from the social graph through graph pattern matching technology. Each match subgraph is a candidate team that meets the conditions.