In recent years, with the rapid development of a computer network, the RDF data volume on the Web rapidly increases, especially a large number of large-scale RDF data sets appear, and the mass data often has a complex network relationship, so that a traditional centralized query scheme cannot rapidly and accurately obtain a query result. The invention discloses a large-scale data parallel query method based on subgraph matching. The method is combined with a distributed platform, and mainly aims to improve the data query efficiency in a large-scale data set. Firstly, an adjacency list storagescheme is adopted for a data graph and a query graph, topological information and attribute information of the graph are fully utilized, and a query process is converted into fields including a judgment process; and then, the problem of matching sequence selection is solved by accurately evaluating the candidate number of the query points of each candidate region, the generation of intermediate results is reduced, and the exploration process of multiple candidate regions can be solved in parallel. Through the mode, the query efficiency can be effectively improved, and an accurate query resultcan be obtained.