This　paper　presents　a　fast　depth　selection　algorithm　for　CTU　(frame　coding　units)　based　on　machine　learning.　In　view　of　the　fast　depth　selection　algorithm　for　CTU　based　on　machine　learning,　due　to　the　lack　of　the　depth　discrimination　in　the　initial　division　of　coding　units　and　the　inefficiencies　of　the　coding　efficiency　caused　by　the　input　feature　selection　of　the　classifier,　The　paper　firstly　design　the　initial　division　depth　prediction　strategy　based　on　the　texture　complexity　and　quantization　parameters　to　skip　some　nonessential　sizes　of　coding　unit　by　analyzing　the　relationship　between　the　texture　complexity　of　the　coding　unit,　the　quantization　parameters　of　encoder　and　the　depth　selection　of　the　coding　unit,　and　by　combining　the　texture　complexity　and　the　quantization　parameters　to　predict　the　initial　dividing　depth　of　the　current　coding　unit.　Secondly,　by　exploring　the　relationship　between　the　bit-rate,　distortion　and　the　depth　selection　of　the　coding　unit,　the　input　characteristics　of　the　classifier　are　determined　and　the　selection　strategy　of　the　coding　unit　termination　depth　based　on　the　bit　rate　and　distortion　is　designed.　Finally,　the　partition　problem　of　the　coding　unit　is　modeled　as　the　problem　of　the　two-element　classification　and　the　nearest　neighbor　classifier　is　used.　By　skipping　the　calculation　process　of　the　time-consuming　rate　distortion　cost,　the　ending　dividing　depth　of　the　current　coding　unit　can　be　judged　in　advance　and　accelerate　the　process　of　the　inter-frame　coding.　Experimental　results　show　that　the　proposed　algorithm　can　decrease　the　34.56%　of　the　frame　encoding　time,　while　maintaining　the　accuracy　of　the　coding　unit　compared　with　HM-15.0.　©　2018　IEEE.