While　rapid　urbanization　endows　people　with　a　modern　life,　it　also　brings　many　urban　diseases　such　as　traffic　congestion　and　uneven　distribution　of　resources.　Taxi　is　one　of　the　main　transportation　methods　for　urban　residents.　Taxi　data　effectively　record　the　spatial　and　temporal　information　of　residents＇　travel　and　can　be　widely　used　for　residents＇　travel　characteristics　mining.　Analyzing　residents＇　travel　characteristics　is　an　important　way　to　solve　and　alleviate　the　increasingly　prominent　urban　problems.　At　present,　rich　research　results　have　been　achieved　in　mining　residents＇　travel　characteristics　using　taxi　OD　flow　data.　Cluster　analysis,　which　is　based　on　taxi　OD　flow　data,　represents　one　of　the　primary　methods　for　uncovering　the　travel　characteristics　of　residents.　But　most　of　the　studies　ignore　the　semantic　information　of　OD　flow.　Urban　POI　data　is　an　important　data　support　for　semantic　extraction　of　OD　flow,　and　semantic　information　can　be　extracted　by　studying　the　relationship　between　OD　flow　and　POI.　To　address　the　problem　of　insufficient　consideration　of　semantic　information　in　spatiotemporal　clustering　algorithms,　a　method　for　extracting　semantics　of　OD　flow　based　on　Global　Vectors　(GloVe)　model　and　density　based　spatiotemporal　semantic　clustering　algorithm　(STS　DBSC　AN,　Spatial　Temporal　Semantic　DBSCAN)　is　proposed　in　this　paper.　Firstly,　OD　flow　semantics　are　extracted　by　combining　POI　visiting　probability　and　GloVe　model,　the　GloVe　model　not　only　fully　considers　the　local　geographic　context　information　of　POIs,　but　also　takes　into　account　its　global　statistical　information　in　the　corpus.　Based　on　this,　a　spatiotemporal　semantic　similarity　measurement　rule　for　OD　flow　is　proposed,　which　comprehensively　considers　temporal,　spatial,　and　semantic　information.　Then,　the　DBSCAN　clustering　algorithm　is　improved　according　to　the　spatiotemporal　semantic　similarity　measurement　rule,　and　the　spatiotemporal　semantic　clustering　of　OD　flow　data　is　realized.　Finally,　analysis　of　travel　characteristics　of　residents　in　Xiamen　island　based　on　OD　flow　semantics　and　spatiotemporal　semantic　clustering,　and　a　total　of　seven　types　of　residents＇　travel　semantics　are　extracted.　Results　show　that:　1)　Residents＇　travel　semantics　are　influenced　by　the　time　factor,　and　the　main　residents’travel　semantics　are　different　in　different　time　periods;　2)　residents＇　travel　hotspots　are　mainly　distributed　in　the　central　developed　area　of　Xiamen　Island;　3)　seven　typical　residents＇　travel　patterns　are　extracted　from　four　main　residents＇　travel　semantics　through　spatiotemporal　semantic　clustering　analysis.　The　results　demonstrate　that　OD　flow　semantic　and　the　spatiotemporal　semantic　clustering　method　can　effectively　mine　the　travel　characteristics　of　urban　residents.　©　2023　Nanjing　University　of　Aeronautics　an　Astronautics.　All　rights　reserved.