As　the　extension　of　high　efficiency　video　coding　(HEVC)　standard,　three　dimensional-HEVC　(3D-HEVC)　is　the　latest　3D　video　coding　standard.　3D-HEVC　adopts　many　complicated　coding　algorithms　to　generate　additional　intermediate　views　for　3D　video　representation,　which　result　in　extremely　high　coding　complexity.　Therefore,　this　paper　proposed　a　fast　depth　intra　coding　approach　to　reduce　the　3D-HEVC　complexity,　which　is　based　on　convolutional　neural　network　(CNN).　First,　we　established　a　database　based　on　the　independent　view　of　the　depth　map,　which　includes　coding　unit　(CU)　partition　data　of　the　depth　map.　Second,　we　constructed　a　depth　edge　classification　CNN　(DEC-CNN)　framework　to　classify　the　edges　for　the　depth　map　and　embedded　the　network　into　a　3D-HEVC　test　platform.　Finally,　we　utilized　the　pixel　value　of　the　binarized　depth　image　to　correct　the　above　classification　results.　The　experimental　results　demonstrated　that　our　approach　can　reduce　the　intra　coding　time　by　72.5%　on　average　under　negligible　degradation　of　coding　performance.　This　result　outperforms　the　other　state-of-the-art　methods　to　reduce　the　coding　complexity　of　3D-HEVC.