This paper proposes an analytical mining tool for big graph data based on MapReduce and bulk synchronous parallel (BSP) computing model. The tool is named Mapreduce and BSP based Graph-mining tool (MBGM). The core of this mining system are four sets of parallel graph-mining algorithms programmed in the BSP parallel model and one set of data extraction-transformation-loading (ETL) algorithms implemented in MapReduce. To invoke these algorithm sets, we designed a workflow engine which optimized for cloud computing. Finally, a well-designed data management function enables users to view, delete and input data in the Hadoop distributed file system (HDFS). Experiments on artificial data show that the components of graph-mining algorithm in MBGM are efficient.