Group　activity　recognition　is　a　subject　with　broad　applications,　and　its　main　challenge　is　to　model　the　interactions　between　individuals.　Existing　algorithms　mostly　model　the　interactions　merely　based　on　holistic　features　of　persons,　which　completely　ignore　the　local　details　and　local　interactions　that　could　be　significant　for　recognition.　In　this　paper,　we　propose　a　novel　part　based　interaction　learning　algorithm　for　group　activity　recognition.　Our　proposed　algorithm　introduces　both　the　physical　structural　information　and　fine-grained　contextual　information　into　representations,　through　exploring　the　intra-and　inter-actor　part　interactions.　Specifically,　a　dual-branch　framework　is　adopted　to　extract　the　appearance　and　motion　features　respectively.　For　each　branch,　we　utilize　the　key　point　detection　technique　for　proper　part　division　and　then　extract　the　part　features.　The　part　features　are　further　enhanced　by　the　transformers　for　intra-and　inter-actor　part　interactions,　and　are　lastly　used　for　group　activity　recognition.　Comparison　with　the　state-of-the-arts　on　two　public　datasets　demonstrate　the　effectiveness　of　our　proposed　algorithm.