The　recognition　precision　of　the　existing　auditory　scene　recognition　algorithms　is　relatively　satisfactory,　but　they　can　only　be　applied　to　several　noise　scenarios,　and　it　can＇t　meet　the　performance　requirements　of　digital　hearing　AIDS　in　complex　environment.　In　order　to　solve　the　above　problems,　scene　recognition　algorithm　based　on　multi-feature　and　weighted　minimum　distance　classifier　is　proposed　in　this　paper.　In　this　algorithm,　the　speech　endpoint　detection　algorithm　based　on　the　band-partitioning　spectral　entropy　and　spectral　energy　is　used　to　divide　the　noisy　speech　into　speech　segment　and　noise　segment.　Then　the　characteristics　such　as　Critical　Band　Ratio　and　band-partitioning　spectral　entropy　as　well　as　adaptive　short-time　zero　crossing　rate　of　each　segment　are　extracted　for　the　weighted　minimum　distance　classifier　to　recognize　the　noise　scenario.　The　experiments　result　shows　that　the　proposed　algorithm　has　strong　robustness　and　high　accuracy.　It＇s　suitable　to　be　applied　in　digital　hearing　AIDS.　©　2016　IEEE.