Isolation　forest　(iForest)　cannot　effectively　detect　local　outliers　and　the　outlier　score　threshold　is　difficult　to　be　precise,　therefore,　an　isolation　forest　outlier　detection　method　based　on　node　evaluation　(NE)　and　maximum　between-class　variance　(Otsu)　was　proposed.　First,　the　scoring　mechanism　was　introduced　into　the　node　depth　and　relative　mass　at　the　same　time　during　the　sample　assessment　process,　so　that　the　algorithm　was　sensitive　to　global　and　local　outliers.　Afterwards,　to　accurately　set　the　score　threshold,　the　Otsu　method　was　used　to　adaptively　determine　the　outlier　score　threshold.　Finally,　the　effectiveness　of　the　proposed　method　was　verified　on　different　datasets.　Results　show　that　the　proposed　method　can　effectively　balance　the　detection　of　global　and　local　outliers,　and　can　improve　the　accuracy　of　detection　of　outliers　in　isolation　forests.　©　2024　Beijing　University　of　Technology.　All　rights　reserved.