To　better　understand　the　characteristics　of　public　transport　commute　passengers　with　different　categories　and　further　to　better　meet　their　customized　travel　demands,　it　is　necessary　to　find　ways　to　identify　public　transport　commuters＇　stability　accurately.　Based　on　revealed　preference　survey　data,　public　transport　smart　card　transaction　and　network　data,　the　travel　chain　of　public　transport　commuter　was　obtained　by　data　processing　and　matching.　According　to　public　transport　travel　behavior　data　of　one　month,　five　characteristic　indexes　were　extracted　to　represent　travel　stability　from　the　aspects　of　activity　point,　travel　space　and　travel　time.　These　feature　indexes　include　the　number　of　non-home　activity　categories,　the　proportion　of　typical　travel　chain,　space　balance　degree,　time　stability　and　time　concentration　degree.　Through　applying　FP-growth　algorithm,　the　association　rules　among　each　characteristics　attributes　for　different　item-sets　were　mined　depending　on　parameters　of　support,　confidence　and　lift　degree.　Then,　three　categories　of　public　transport　commute　passengers　with　apparently　different　stabilities　were　identified.　Finally,　the　rationality　of　the　proposed　identification　method　was　verified.　The　study　results　contribute　to　developing　customized　and　targeted　supply　and　demand　management　strategy　for　public　transport　travel,　which　would　further　help　to　improve　the　efficient　and　delicacy　service　level　of　public　transport.　©　2019,　Jilin　University　Press.　All　right　reserved.