Commuters　are　the　stable　travel　group　for　the　public　transportation　(PT)　service　system.　Accurately　identifying　the　PT　commuters　is　conducive　to　promoting　PT　service　quality　and　development　of　urban　sustainable　transportation.　This　paper　extracts　individual　PT　travel　chain　information　and　constructs　individual　travel　knowledge　graphs　of　PT　passengers　based　on　the　association　matching　algorithm　and　the　theory　of　multilayer　planning.　A　mixed　dataset　is　formed　by　associating　individual　travel　chains　with　travel　survey　data.　Seven　travel　characteristic　indicators　regarding　travel　performance　and　spatiotemporal　travel　characteristics　are　extracted.　The　identification　model　of　PT　commuters　is　developed　based　on　a　three-layer　backpropagation　neural　network　(BPNN).　The　optimal　model　structure　of　neuron　node　number,　transfer　function,　and　learning　rate　are　discussed　quantitatively　according　to　the　minimization　of　model　errors.　The　evaluation　indexes　of　overall　accuracy　and　kappa　coefficient　of　the　constructed　model　are　94.5%　and　87.9%　separately.　The　results　indicate　that　the　model　identification　accuracy　is　acceptable,　and　the　proposed　characteristic　indicators　and　systematic　modelling　procedure　are　effective.　Then,　the　model　performance　is　compared　with　the　other　five　machine　learning　models　further.　The　results　confirm　that　the　proposed　model　has　a　better　identification　accuracy　and　viability,　and　the　model　performance　will　improve　with　the　increase　of　the　sample　size.