The invention discloses a super-large-scale RDF graph data division and parallel distribution processing method, including: preprocessing the original RDF graph data, generating a corresponding hash dictionary file and shaping three-table data, and shaping the three-table data Convert into an association matrix M; establish a hypergraph model of the association matrix M, in which the subject, predicate and object of M are hyperedges, and data related to hyperedges are hyperedge data; judge the RDF graph Whether the data is a connected graph or a disconnected graph, if it is a disconnected graph, divide the disconnected graph into multiple connected graphs; based on the hypergraph model, the concurrent breadth traverses and equally divides the hyperedge data on the placement path, and divides the hyperedge The data is classified and sorted and equally divided into K parts and placed on K slave nodes. At the same time, the mapping relationship between hyperedge data and slave nodes is established. The invention has fast division speed, high division quality, balanced data and task load, high parallelism and high speed of query processing.