Coding　tree　unit　(CTU)　partition　technique　provides　excellent　compression　performance　for　HEVC　at　the　expense　of　increased　coding　complexity.　Therefore,　a　fast　intra　coding　algorithm　based　CTU　depth　range　prediction　is　proposed　to　reduce　the　complexity　of　HEVC　intra　coding　herein.　First,　simple　CTUs　and　complex　CTUs　are　defined　in　line　with　their　texture　complexity,　which　are　limited　to　different　depth　ranges.　Then,　the　convolutional　neural　network　architecture　for　HEVC　intra　depth　range　(HIDR-CNN)　decision-making　is　proposed.　It　is　used　for　CTU　classification　and　depth　range　restriction.　Last,　the　optimal　CTU　partition　is　achieved　by　recursive　rate　distortion　(RD)　cost　calculation　in　the　depth　range.　Experimental　results　show　that　the　proposed　algorithm　can　achieve　average　27.54%　encoding　time　reduction　with　negligible　RD　loss　compared　with　HM　16.9.　The　proposed　algorithm　devotes　to　promote　popularization　of　HEVC　in　real-time　environments.