Community　detection　algorithms　based　on　game　theory　model　the　formation　of　communities　as　a　game.　When　the　game　reaches　equilibrium,　the　community　structure　is　revealed.　These　algorithms　provide　good　interpretability　for　the　community　formation　process,　thus　attracting　the　attention　of　scholars.　In　particular,　methods　based　on　non-cooperative　games　emphasize　independent　decision-making　by　individuals,　exhibiting　good　adaptability　to　dynamic　network　changes.　These　methods　discover　new　community　structures　by　updating　node　strategies.　However,　existing　community　detection　algorithms　based　on　non-cooperative　games　face　challenges　such　as　randomness　in　static　networks　and　the　need　for　repeated　global　games　in　dynamic　networks.　To　address　these　issues,　the　paper　investigates　an　Incremental　Dynamic　Community　Detection　Algorithm　based　on　Local　Rapid　Update　under　Non-cooperative　Game　Theoretic　Framework　(IDCDG).　On　the　one　hand,　we　propose　to　play　the　game　according　to　the　importance　of　the　nodes　from　small　to　large,　to　use　the　utility　function　based　on　local　information,　and　to　use　a　greedy　strategy　to　choose　the　best　community　affiliation.　On　the　other　hand,　we　give　the　definitions　of　active　nodes　and　non-active　nodes,　initialize　the　active　nodes,　and　reinitialize　the　non-active　nodes　when　the　historical　community　is　not　connected.　Finally,　we　propose　a　fast　game　queue　and　prove　that　when　the　queue　is　empty,　the　game　reaches　equilibrium.　The　experimental　results　on　real　networks　and　synthetic　networks　verify　the　effectiveness　of　our　algorithm.　©　2025　IEEE.