In　order　to　solve　the　problem　that　semantic　information　is　not　fully　considered　in　existing　OD　flow　clustering　algorithms　and　it　is　difficult　to　mine　OD　flow　semantic　information,　this　paper　proposes　an　OD　flow　clustering　algorithm　based　on　the　Latent　Dirichlet　Allocation　(LDA)　model　and　ant　colony　optimization　algorithm.　Firstly,　the　LDA　Topic　model　is　used　to　extract　OD　flows＇　semantics,　and　the　JS　divergence　(Jensen-Shannon　divergence)　is　used　to　quantify　the　semantic　similarity　between　OD　flows.　We　also　propose　a　spatiotemporal　semantic　similarity　calculation　method　that　is　constructed　by　integrating　temporal,　spatial,　and　semantic　similarity,　which　provides　data　basis　for　flow　clustering.　Then,　the　graph　network　data　structure　is　constructed　according　to　the　spatiotemporal　semantic　similarity,　and　the　Gaussian　function　mapping　and　the　connected　component　of　the　graph　are　used　to　simplify　the　data　and　eliminate　the　noise　data.　Based　on　the　idea　of　CFDP　algorithm　(Clustering　by　fast　search　and　find　of　density　peaks　algorithm),　the　intermediate　centrality　of　nodes　is　used　to　optimize　the　selection　strategy　of　the　initial　position　of　ant　colony.　Finally,　the　Multi-path　Normalized　Cut　(MNCUT)　graph　criterion　is　used　to　strengthen　the　purpose　of　ant　colony　search,　optimize　the　clustering　effect　of　ant　colony　search,　and　realize　the　spatiotemporal　semantic　clustering　for　OD　flow　direction.　Taking　Xiamen　taxi　open　data　set　and　Xiamen　map　POI　data　as　examples,　the　proposed　method　is　verified.　The　experimental　results　show　that:　(1)　The　proposed　method　can　effectively　extract　the　semantic　information　of　flow　direction　and　measure　the　similarity　degree　between　flow　directions　more　comprehensively　compared　with　the　existing　methods;　(2)　The　Gaussian　function　mapping　strategy　and　graph　connected　component　feature　are　adopted　to　effectively　eliminate　the　noise　in　the　flow　direction　data,　which　saves　the　computational　cost　of　undirected　graph　construction　effectively　by　88.5%~88.8%　of　the　running　time;　(3)　Compared　with　the　existing　algorithms,　the　clustering　division　of　the　proposed　algorithm　is　more　precise,　and　the　correlation　analysis　of　flow　semantics　can　be　carried　out　conveniently　and　effectively.　©　2022,　Science　Press.　All　right　reserved.