Community　detection　on　high-mixed　networks　has　been　a　challenging　problem　for　complex　network　researchers.　In　a　Lancichinetti-Fortunato-Radicchi　(LFR)　network　with　a　mixing　parameter　mu　greater　than　or　equal　to　0.5,　the　quality　of　the　communities　partitioned　by　currently　available　algorithms　will　decrease　rapidly　with　increasing　mu.　To　address　this　issue,　we　propose　a　label　propagation　algorithm　on　high-mixed　networks,　called　LPA-HM,　for　community　detection.　In　our　algorithm,　the　initial　node　labels　are　preprocessed　using　the　number　of　common　neighbors　of　the　nodes,　which　greatly　reduces　the　initial　number　of　labels　and　thus　improves　the　quality　of　the　subsequent　label　propagation　process.　During　the　label　propagation　stage,　each　node　is　given　the　label　that　is　shared　by　the　maximum　number　of　its　neighbors.　If　there　are　several　labels　that　meet　this　requirement,　the　influence　of　the　labels＇　nodes　is　calculated,　and　the　label　with　the　maximum　total　influence　is　selected　as　the　label　of　the　current　node.　Early　stop　conditions　based　on　modularity　and　run-to-run　changes　in　the　number　of　detected　communities　are　incorporated　in　the　algorithm　to　prevent　label　overpropagation.　The　communities　that　fail　to　satisfy　the　definition　of　weak　communities　are　merged　with　their　most　similar　neighboring　communities.　In　experiments　based　on　real　networks　and　LFR　networks,　it　is　found　that　the　LPA-HM　algorithm　is　well　suited　to　community　detection　in　a　variety　of　networks.　In　a　high-mixed　LFR　network　with　mu　=　0.7,　the　NMI　measure　of　the　LPA-HM　algorithm＇s　community　detection　performance　is　still　greater　than　0.9.