Recently,　human　activity　recognition　(HAR)　has　gained　significant　attention　as　a　research　field,　leading　to　the　development　of　diverse　technologies　driven　by　its　broad　range　of　application　scenarios.　Radar　technology　has　attracted　much　attention　because　of　its　unique　advantages　such　as　not　being　limited　by　environmental　conditions　such　as　light,　shadow,　and　occlusion.　In　this　article,　a　continuous　HAR　system　based　on　multidomain　radar　data　fusion　(CMDN)　is　proposed.　Firstly,　in　order　to　capture　more　detailed　motion　features　of　the　human　body,　we　apply　the　short-time　fractional　Fourier　transform　(STFrFT)　to　map　radar　data　into　the　fractional　domain,　yielding　a　novel　representation　of　human　motion.　Secondly,　we　develop　an　activity　detector　based　on　variable　window　length　short-time　average/long-time　average　(VW-STA/LTA)　to　accurately　identify　the　start/end　points　of　continuous　human　actions,　addressing　the　challenge　of　difficult　sequence　segmentation　in　continuous　activity　recognition　tasks.　Finally,　based　on　the　multi-input　multitask　(MIMT)　recognition　network,　the　features　of　each　domain　are　processed　in　parallel,　and　multiple　input　representations　are　fused　to　obtain　the　continuous　activity　classification　results　with　high　precision.