Station-free　shared　bike　(SFSB)　is　a　new　travel　mode　that　shared　bikes　are　allowed　to　park　in　any　proper　places.　It　implies　that　the　users　are　more　likely　to　park　the　SFSB　as　close　as　their　destinations.　This　advantage　makes　the　SFSB　data　to　be　an　ideal　source　to　understand　the　land-use　distribution.　Inspired　by　the　idea　in　text　mining,　this　paper　proposes　a　topic-based　two-stage　SFSB　data　mining　algorithm　to　understand　the　SFSB　user＇s　travel　behavior　and　to　discover　city　functional　regions.　A　region,　a　function　and　human　mobility　patterns　are　treated　as　a　document,　a　topic　and　words,　respectively.　Then,　a　region　is　represented　by　a　distribution　of　functions,　and　a　function　is　featured　by　a　distribution　of　mobility　patterns.　The　point-of-interest　data　is　combined　to　annotate　the　clustered　regions　to　discover　the　real　functions.　At　last,　the　proposed　method　is　tested　using　14-day　SFSB　data　in　Beijing　and　the　results　are　estimated　by　the　Satellite　Map　data.　The　proposed　methods　and　the　results　can　be　applied　to　infer　the　individual＇s　travel　purpose　through　functional　regions　and　to　improve　land-use　planning,　etc.　(C)　2020　Elsevier　Ltd.　All　rights　reserved.