Group　activity　recognition　(GAR)　remains　challenging　due　to　the　diverse　interactions　among　individuals　across　different　group　activities.　Therefore,　fully　exploring　the　complex　spatiotemporal　interactions　among　multiple　individuals　in　video　scenes　is　key　to　GAR.　To　address　this　issue,　we　propose　a　novel　GAR　framework,　which　hierarchically　fuses　features　at　different　levels　from　complementary　spatiotemporal　dual　paths,　enhancing　the　spatiotemporal　interactions　between　individuals.　Moreover,　different　from　previous　works,　our　framework　further　incorporates　a　unique　contrastive　loss　function,　which　upholds　consistency　of　the　same　individuals’　representations　across　paths　at　the　instance　level　and　amplifies　distinction　among　features　of　individuals　from　distinct　classes　at　the　category　level.　This　unique　loss　can　help　reduce　noise　interference　in　the　representations　of　the　dual　path　and　avoid　confusion　between　individual　categories.　Our　method　has　been　extensively　evaluated　on　public　datasets,　demonstrating　its　superiority.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.