The　performance　of　the　existing　speech　enhancement　algorithms　is　not　ideal　in　low　signal-to-noise　ratio　(SNR)　non-stationary　noise　environments.　In　order　to　resolve　this　problem,　a　novel　speech　enhancement　algorithm　based　on　multi-feature　and　adaptive　mask　with　deep　learning　is　presented　in　this　paper.　First,　we　construct　a　new　feature　called　multi-resolution　auditory　cepstral　coefficient　(MRACC).　This　feature　which　is　extracted　from　four　cochleagrams　of　different　resolutions　can　capture　the　local　information　and　spectrotemporal　context　and　reduce　the　algorithm　complexity.　Second,　an　adaptive　mask　(AM)　which　can　track　noise　change　for　speech　enhancement　is　put　forward.　The　AM　can　flexibly　combine　the　advantages　of　an　ideal　binary　mask　(IBM)　and　an　ideal　ratio　mask　(IRM)　with　the　change　of　SNR.　Third,　a　deep　neural　network　(DNN)　architecture　is　used　as　a　nonlinear　function　to　estimate　adaptive　mask.　And　the　first　and　second　derivatives　of　MRACC　and　MRACC　are　used　as　the　input　of　the　DNN.　Finally,　the　estimated　AM　is　used　to　weight　the　noisy　speech　to　achieve　enhanced　speech.　Experimental　results　show　that　the　proposed　algorithm　not　only　further　improves　speech　quality　and　intelligibility,　but　also　suppresses　more　noise　than　the　contrast　algorithms.　In　addition,　the　proposed　algorithm　has　a　lower　complexity　than　the　contrast　algorithms.