The　latest　standard,　Versatile　Video　Coding　(VVC),　doubles　the　coding　efficiency　over　the　previous　generation　standard.　However,　better　performance　is　at　the　cost　of　a　sharp　increase　in　coding　complexity.　In　order　to　reduce　the　complexity　of　VVC　intra　coding,　this　paper　proposes　a　multi-stage　block　partition　decision　framework　based　on　deep　learning.　First,　we　propose　a　three-stage　redundant　modes　removal　framework　that　decreases　the　number　of　modes　checked　in　the　brute-force　process.　Then,　we　build　a　lightweight　CNN　to　complete　the　classification　task　of　each　stage.　To　reduce　the　burden　of　CNN　and　adapt　to　different　Coding　Unit　(CU)　sizes,　we　pre-process　the　luminance　component　of　CU　and　use　the　results　as　input　of　the　network.　Finally,　the　multi-threshold　adjusting　scheme　is　proposed　for　trading　off　complexity　reduction　with　the　bitrate　increase.　The　experimental　results　shows　our　method　can　reduce　the　encoding　time　ranging　from　16.93%　to　69.40%　with　the　bit-rate　increase　ranging　from　0.31%　to　3.59%.　Such　results　demonstrate　that　our　method　has　superior　performance　with　a　wide　range　of　adjustments　compared　with　other　state-of-the-art　methods.