The invention discloses a cluster-based graph data division method. The method comprises the steps of determining a degree value of each vertex in V vertexes comprised in graph data, wherein the graph data comprises E edges; according to the degree value of each vertex, sorting the V vertexes according to a degree value sequence from big to small, and determining M vertexes with the degree values greater than a first threshold; according to the V vertexes, the E edges and the M vertexes, determining T paths; attributing the vertexes which T paths do not comprise in the V vertexes to the T paths, forming the T paths after the attribution, or creating N paths by the T paths; according to the M vertexes, the T paths and the N paths, determining M clusters; and according to an association degree among the M clusters and a preset divided block number P, dividing the M clusters of the graph data into P divided blocks. According to the method, the vertexes with the relatively big degree values serve as end pintos of the paths, so that the repetition rate of graph data division can be reduced; the graph scale is reduced by taking the paths as units, so that the memory overhead is reduced; and large-scale graph data can be processed.