Coding　tree　unit　(CTU)　partition　technique　is　one　of　the　most　advanced　techniques,　which　devotes　to　the　excellent　performance　of　High　Efficiency　Video　Coding　(HEVC).　However,　the　enhancement　of　coding　performance　is　at　the　expense　of　increased　coding　complexity.　To　reduce　the　complexity　of　HEVC　intracoding,　a　fast　CTU　depth　decision　algorithm　based　on　texture　features　and　convolutional　neural　network　(CNN)　classification　technique　is　proposed　herein.　First,　the　relationship　between　texture　complexity　and　coding　unit　depth　is　explored.　Based　on　this,　CTUs　are　divided　into　simple　CTUs　and　complex　CTUs　in　line　with　their　texture　complexity,　which　are　limited　to　different　depth　ranges.　Then,　the　CNN　for　HEVC　intradepth　range　(HIDR-CNN)　decision-making　is　proposed,　which　is　used　for　CTU　classification　and　depth　range　restriction.　Finally,　the　optimal　CTU　partition　is　achieved　by　recursive　rate-distortion　cost　calculation　in　the　depth　range.　Experimental　results　show　that　the　proposed　algorithm　can　yield　average　27.54%　encoding　time　reduction　with　0.99%　BDBR　gain　or　0.05　dB　BDPSNR　loss　compared　with　HM　16.9.　The　proposed　algorithm　contributes　to　promote　HEVC　coding　efficiency　under　real-time　environments.