MapReduce computation model can not satisfy the efficiency requirement of graph data processing in the Hadoop cloud platform. In order to address the issue, a novel computation framework of graph data processing, called MyBSP (My Bulk Synchronous Parallel), was proposed. MyBSP is similar with Pregel developed from Google. Firstly, the running mechanism and shortcomings of MapReduce were analyzed. Secondly, the structure, workflow and principal interfaces of MyBSP framework were described. Finally, the principle of the PageRank algorithm for graph data processing was analyzed. Subsequently, the design and implementation of the PageRank algorithm for graph data processing were presented. The experimental results show that, the iteration processing performance of graph data processing algorithm based on the MyBSP framework is raised by 1.9-3 times compared with the algorithm based on MapReduce. Furthermore, the execution time of the MyBSP algorithm is reduced by 67% compared with MapReduce approach. Thus, MyBSP can efficiently meet the application prospect of graph data processing.