To　obtain　elaborate　travel　characteristics　and　better　meet　travel　demands　for　different　public　transport　passengers,　it　is　necessary　to　find　ways　identifying　public　transport　commuter　accurately.　Based　on　public　transport　smart　card　transaction　and　network　data,　travel　chain　is　obtained　by　data　processing　and　matching.　Taking　travel　behavior　data　of　April,　2017　in　Beijing,　China,　individual　travel　graph　is　constructed　by　adopting　multi-layer　planning　theory.　Seven　feature　indexes　are　extracted　from　individual　travel　graph　and　set　as　input　for　passenger　classification　model.　Revealed　Preference　survey　is　conducted　to　collect　travel　behavior　category　attributes,　which　is　the　output　of　classification　model.　A　back　propagation　neuron　networks　based　public　transport　passenger　classification　model　is　constructed.　Validation　results　indicate　that　the　average　classification　accuracy　and　Kappa　coefficient　are　94.5%　and　0.879,　respectively.　The　study　results　contribute　to　identify　public　transport　passengers　of　different　types　accurately　and　further　support　to　optimize　public　transport　operating　and　improve　service　level　precisely.　Copyright　©　2018　by　Science　Press.