Community　detection　is　an　extremely　important　technology　for　today＇s　rapidly　evolving　data　mining　and　exploratory　analysis.　Many　community　detection　algorithms　have　been　proposed　and　applied.　Among　them,　the　label　propagation　algorithm　(LPA)　is　widely　used　due　to　its　closeness　to　linear　time　complexity　and　its　simple　characteristics.　However,　the　results　of　detecting　the　community　were　found　to　be　random　when　using　LPA.　Based　on　previous　improvements　to　the　LPA,　this　article　proposes　a　new　LPA　(NOHLPA)　that　joins　multilayer　neighborhood　overlap　and　historical　label　similarity.　The　NOHLPA　considers　both　the　node　update　order　and　label　selection　rules.　We　cited　the　label　entropy　as　the　basis　for　node　update　order　and　defined　multilayer　neighborhood　overlap　and　historical　label　similarity　for　calculating　node　preferences　to　devise　more　appropriate　label　selection　rules.　We　test　the　NOHLPA　on　five　real　datasets　and　compare　NOHLPA　with　different　combinations　of　algorithms.　The　experimental　results　show　that　the　NOHLPA　effectively　improves　the　accuracy　of　community　partitioning　while　ensuring　stability.