This　paper　presents　an　effective　machine　learning-based　depth　selection　algorithm　for　CTU　(Coding　Tree　Unit)　in　HEVC　(High　Efficiency　Video　Coding).　Existing　machine　learning　methods　are　limited　in　their　ability　in　handling　the　initial　depth　decision　of　CU　(Coding　Unit)　and　selecting　the　proper　set　of　input　features　for　the　depth　selection　model.　In　this　paper,　we　first　propose　a　new　classification　approach　for　the　initial　division　depth　prediction.　In　particular,　we　study　the　correlation　of　the　texture　complexity,　QPs　(quantization　parameters)　and　the　depth　decision　of　the　CUs　to　forecast　the　original　partition　depth　of　the　current　CUs.　Secondly,　we　further　aim　to　determine　the　input　features　of　the　classifier　by　analysing　the　correlation　between　depth　decision　of　the　CUs,　picture　distortion　and　the　bit-rate.　Using　the　found　relationships,　we　also　study　a　decision　method　for　the　end　partition　depth　of　the　current　CUs　using　bit-rate　and　picture　distortion　as　input.　Finally,　we　formulate　the　depth　division　of　the　CUs　as　a　binary　classification　problem　and　use　the　nearest　neighbor　classifier　to　conduct　classification.　Our　proposed　method　can　significantly　improve　the　efficiency　of　interframe　coding　by　circumventing　the　traversing　cost　of　the　division　depth.　It　shows　that　the　mentioned　method　can　reduce　the　time　spent　by　34.56%　compared　to　HM-16.9　while　keeping　the　partition　depth　of　the　CUs　correct.