Since　the　era　of　we-media,　live　video　industry　has　shown　an　explosive　growth　trend.　For　large-scale　live　video　streaming,　especially　those　containing　crowd　events　that　may　cause　great　social　impact,　how　to　identify　and　supervise　the　crowd　activity　in　live　video　streaming　effectively　is　of　great　value　to　push　the　healthy　development　of　live　video　industry.　The　existing　crowd　activity　recognition　mainly　uses　visual　information,　rarely　fully　exploiting　and　utilizing　the　correlation　or　external　knowledge　between　crowd　content.　Therefore,　a　crowd　activity　recognition　method　in　live　video　streaming　is　proposed　by　3D-ResNet　and　regional　graph　convolution　network　(ReGCN).　(1)　After　extracting　deep　spatiotemporal　features　from　live　video　streaming　with　3D-ResNet,　the　region　proposals　are　generated　by　region　proposal　network.　(2)　A　weakly　supervised　ReGCN　is　constructed　by　making　region　proposals　as　graph　nodes　and　their　correlations　as　edges.　(3)　Crowd　activity　in　live　video　streaming　is　recognised　by　combining　the　output　of　ReGCN,　the　deep　spatiotemporal　features　and　the　crowd　motion　intensity　as　external　knowledge.　Four　experiments　are　conducted　on　the　public　collective　activity　extended　dataset　and　a　real-world　dataset　BJUT-CAD.　The　competitive　results　demonstrate　that　our　method　can　effectively　recognise　crowd　activity　in　live　video　streaming.