Focusing　on　the　problem　that　conventional　decision　tree　(DT)　model　lacks　of　a　probabilistic　background,　the　Bayesian　inference　was　introduced　into　DT,　and　thus　a　decision　tree　(BDT)　model　based　on　Bayesian　inference　was　proposed.　Under　the　premise　that　the　prior　and　likehood　of　contained　parameters　needed　to　be　determined　has　been　assumed,　the　posterior　of　parameters　are　obtained　through　Bayesian　inference.　Then　the　posterior　is　sampled　by　using　reversible　jump　Markov　chain　Monte　Carlo　algorithm,　and　finally　the　confidence　level　of　the　samples　belonged　to　certain　class　is　solved　to　avoid　any　arbitrary　decision.　In　BDT　model,　the　splitting　and　pruning　is　substituted　by　sampling,　both　are　intuitive　and　flexible,　and　different　tree　structures　and　recursive　partition　schemes　are　considered　so　as　to　increase　the　accuracy　rate　of　classification.　The　experimental　results　show　that　the　average　classification　accuracy　is　improved　by　1.7%-3.5%　compared　to　DT　model.