With　the　continuous　development　of　human　pose　estimation　techniques,　researchers　have　gradually　applied　them　to　the　field　of　repetitive　action　counting,　resulting　in　pose-level　methods.　However,　the　current　researches　on　the　pose-level　are　still　limited.　Therefore,　this　paper　proposes　a　simple　but　efficient　Body　Inter-intra-parts　Graph　Convolutional　Network　(BIGC-Net).　Specifically,　two　core　modules　are　developed　in　BIGC-Net:　the　Global　Inter-Part　Feature　Learning　Module　(GIFL-Module)　and　the　Salient　Intra-Part　Feature　Learning　Module　(SIFL-Module).　Unlike　previous　pose-level　methods,　which　only　model　human　joints　globally　and　ignore　local　details.　Instead,　we　innovatively　introduce　the　concept　of　body　parts　with　Graph　Convolutional　Networks　(GCN)　to　the　repetitive　action　counting　task.　Based　on　the　natural　topology　of　the　human　body,　we　divide　the　joints　into　multiple　inter-intra-parts,　each　of　which　is　regarded　as　a　subgraph　to　form　the　overall　graph　structure.　The　complete　action　is　then　achieved　by　the　collaborative　operation　between　different　subgraphs,　thus　modelling　the　action　execution　process　more　accurately.　Therefore,　the　GIFL-Module　is　designed　to　capture　the　global　collaborative　relationships　between　the　subgraphs.　However,　since　the　body　joints　are　segmented　into　multiple　parts,　this　segmentation　may　ignore　the　variation　of　local　detail　information　within　the　subgraphs.　To　address　this　issue,　the　SIFL-Module　aims　to　capture　the　local　interdependencies　between　joints　within　the　subgraphs,　and　the　ability　to　focus　on　the　most　salient　features　of　the　subgraphs　as　it　moves.　The　collaboration　of　these　two　modules　further　enhances　the　feature　representation　capability.　Finally,　extensive　experimental　results　on　the　challenging　benchmark　datasets　(RepCount-pose,　UCFRep-pose,　and　Countix-Fitness-pose)　show　that　the　proposed　BIGC-Net　achieves　excellent　performance.　©　2025　Elsevier　Ltd