The　weight　of　antecedent　attributes　can＇t　work　accurately　in　the　linear　combinational　belief　rule　based　system　usually.　Simultaneously,　with　an　increase　in　the　number　of　evaluation　ranks,　the　new　weight　activation　formula　will　have　negative　effects　on　results.　Aiming　at　the　above　drawbacks,　this　paper　proposes　a　two-value　and　multi-base　reasoning　method　based　on　the　existing　belief　rule　based　inference　classification　algorithm　to　improve　the　belief　rule　based　decision　system.　The　evaluation　of　belief　rules　in　the　conclusion　are　divided　into　two　ranks　firstly,　which　means　making　a　two-value　judgment　on　a　decision　problem.　Then　many　belief　rule　bases　are　set　to　solve　some　sub　problems　simultaneously.　Finally　results　of　many　sub　problems　by　multi-base　reasoning　method　are　mixed　to　solve　the　classification　problem.　Experimental　results　show　the　feasibility　and　effectiveness　of　the　proposed　belief　rule　base　reasoning　classfication　method.　©　2018　by　Journal　of　Data　Acquisition　and　Processing.