Community　structure　is　an　important　characteristic　of　complex　networks.　Uncovering　communities　in　complex　networks　is　currently　a　hot　research　topic　in　the　field　of　network　analysis.　Local　community　detection　algorithms　based　on　seed-extension　are　widely　used　for　addressing　this　problem　because　they　excel　in　efficiency　and　effectiveness.　Compared　with　global　community　detection　methods,　local　methods　can　uncover　communities　without　the　integral　structural　information　of　complex　networks.　However,　they　still　have　quality　and　stability　deficiencies　in　overlapping　community　detection.　For　this　reason,　a　local　community　detection　algorithm　based　on　internal　force　between　nodes　is　proposed.　First,　local　degree　central　nodes　and　Jaccard　coefficient　are　used　to　detect　core　members　of　communities　as　seeds　in　the　network,　thus　guaranteeing　that　the　selected　seeds　are　central　nodes　of　communities.　Second,　the　node　with　maximum　degree　among　seeds　is　pre-extended　by　the　fitness　function　every　time.　Finally,　the　top　k　nodes　with　the　best　performance　in　pre-extension　process　are　extended　by　the　fitness　function　with　internal　force　between　nodes　to　obtain　high-quality　communities　in　the　network.　Experimental　results　on　both　real　and　artificial　networks　show　that　the　proposed　algorithm　can　uncover　communities　more　accurately　than　all　the　comparison　algorithms.　©　2019,　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature.