Focusing on low accuracy and high costs of backtracking-based subgraph query algorithm applied to large-scale graphs, a Spark-based Subgraph Query Matching (SQM) algorithm was proposed to improve query accuracy and reduce query overhead for large graphs. The data graph was firstly filtered according to structure information, and the query graph was divided into basic query units. Then each basic query unit was matched and joined together. Finally, the algorithm's efficiency was improved and search space was reduced by parallelization. The experimental results show that compared with Stwig (Sub twig) algorithm and TurboISO algorithm, SQM algorithm can increase the speed by 50% while ensuring the same query results.