Versatile　video　coding　(H.266/VVC),　which　was　newly　released　by　the　Joint　Video　Exploration　Team　(JVET),　introduces　quad-tree　plus　multi　type　tree　(QTMT)　partition　structure　on　the　basis　of　quad-tree　(QT)　partition　structure　in　High　Efficiency　Video　Coding　(H.265/HEVC).　More　complicated　coding　unit　(CU)　partitioning　processes　in　H.266/VVC　significantly　improve　video　compression　efficiency,　but　greatly　increase　the　computational　complexity　compared.　The　ultra-high　encoding　complexity　has　obstructed　its　real-time　applications.　In　order　to　solve　this　problem,　a　CU　partition　algorithm　using　convolutional　neural　network　(CNN)　is　proposed　in　this　paper　to　speed　up　the　H.266/VVC　CU　partition　process.　Firstly,　64　??　64　CU　is　divided　into　smooth　texture　CU,　mildly　complex　texture　CU　and　complex　texture　CU　according　to　the　CU　texture　characteristics.　Second,　CU　texture　complexity　classification　convolutional　neural　network　(CUTCC-CNN)　is　proposed　to　classify　CUs.　Finally,　according　to　the　classification　results,　the　encoder　is　guided　to　skip　different　RDO　search　process.　And　optimal　CU　partition　results　will　be　determined.　Experimental　results　show　that　the　proposed　method　reduces　the　average　coding　time　by　32.2%　with　only　0.55%　BD-BR　loss　compared　with　VTM　10.2.