Local　Outlier　Factor　(LOF)　outlier　detecting　algorithm　has　good　accuracy　in　detecting　global　and　local　outliers.　However,　the　algorithm　needs　to　traverse　the　entire　dataset　when　calculating　the　local　outlier　factor　of　each　data　point,　which　adds　extra　time　overhead　and　makes　the　algorithm　execution　inefficient.　In　addition,　if　the　K　-distance　neighborhood　of　an　outlier　point　P　contains　some　outliers　that　are　incorrectly　judged　by　the　algorithm　as　normal　points,　then　P　may　be　misidentified　as　normal　point.　To　solve　the　above　problems,　this　paper　proposes　a　Neighbor　Entropy　Local　Outlier　Factor　(NELOF)　outlier　detecting　algorithm.　Firstly,　we　improve　the　Self-Organizing　Feature　Map　(SOFM)　algorithm　and　use　the　optimized　SOFM　clustering　algorithm　to　cluster　the　dataset.　Therefore,　the　calculation　of　each　data　point＇s　local　outlier　factor　only　needs　to　be　performed　inside　the　small　cluster.　Secondly,　this　paper　replaces　the　K　-distance　neighborhood　with　relative　K　-distance　neighborhood　and　utilizes　the　entropy　of　relative　K　neighborhood　to　redefine　the　local　outlier　factor,　which　improves　the　accuracy　of　outlier　detection.　Experiments　results　confirm　that　our　optimized　SOFM　algorithm　can　avoid　the　random　selection　of　neurons,　and　improve　clustering　effect　of　traditional　SOFM　algorithm.　In　addition,　the　proposed　NELOF　algorithm　outperforms　LOF　algorithm　in　both　accuracy　and　execution　time　of　outlier　detection.