Objectives:　Compared　with　the　outdoor　space,　the　indoor　three-dimensional　space　structure　is　complex,　and　indoor　crowd　gathering　is　more　likely　to　lead　to　safety　accidents.As　the　development　of　indoor　positioning　technology,　it　is　possible　to　collect　indoor　trajectory,　which　also　provides　an　important　data　source　for　the　identification　of　indoor　crowd　gathering　area.　Methods:　We　propose　a　novel　method　called　IndoorSRC　(indoor　simplification　reconstruction　cluster)　to　detect　indoor　crowd　gathering　areas.　Firstly,　a　new　indoor　user　trajectory　simplification　algorithm,　indoor　spatial-temporal　agglomerative　nesting　(Indoor-STAGNES),　is　designed　to　identify　indoor　user　stay　points　and　simplify　indoor　user＇s　trajectory.　Then,　an　indoor　trajectory　reconstruction　method　based　on　Kalman　filter　is　constructed　to　realize　the　alignment　and　resampling　of　indoor　user　trajectories.　Finally,　a　new　indoor　space-time　density　clustering　algorithm:　Indoor　spatial-temporal　ordering　points　to　identify　the　clustering　structure　(Indoor-STOPTICS)　is　proposed　to　find　indoor　three-dimensional　space-time　crowd　gathering　area.　Results:The　real　shopping　mall　indoor　trajectory　data　are　used　for　experimental　analysis.　The　experimental　results　show　that:(1)The　gathering　areas　in　the　mall　are　mostly　concentrated　in　the　noon　time　and　are　mainly　located　in　the　dining　area.(2)　Compared　with　the　traditional　outdoor　identification　method,　the　error　of　recognition　can　be　reduced　by　23.7%　in　the　case　of　IndoorSRC　with　little　difference　in　running　time.　Conclusions:　IndoorSRC　can　provide　an　effective　supplement　to　the　indoor　crowd　gathering　area　identification,　and　provide　technical　support　for　early　warning　and　emergency　rescue　of　indoor　safety　accidents.　©　2021,　Editorial　Board　of　Geomatics　and　Information　Science　of　Wuhan　University.　All　right　reserved.