The　ever-growing　multimedia　traffic　has　underscored　the　importance　of　effective　multimedia　codecs.　Among　them,　the　up-to-date　lossy　video　coding　standard,　Versatile　Video　Coding　(VVC),　has　been　attracting　attentions　of　video　coding　community.　However,　the　gain　of　VVC　is　achieved　at　the　cost　of　significant　encoding　complexity,　which　brings　the　need　to　realize　fast　encoder　with　comparable　Rate　Distortion　(RD)　performance.　In　this　paper,　we　propose　to　optimize　the　VVC　complexity　at　intra-frame　prediction,　with　a　two-stage　framework　of　deep　feature　fusion　and　probability　estimation.　At　the　first　stage,　we　employ　the　deep　convolutional　network　to　extract　the　spatial-temporal　neighboring　coding　features.　Then　we　fuse　all　reference　features　obtained　by　different　convolutional　kernels　to　determine　an　optimal　intra　coding　depth.　At　the　second　stage,　we　employ　a　probability-based　model　and　the　spatial-temporal　coherence　to　select　the　candidate　partition　modes　within　the　optimal　coding　depth.　Finally,　these　selected　depths　and　partitions　are　executed　whilst　unnecessary　computations　are　excluded.　Experimental　results　on　standard　database　demonstrate　the　superiority　of　proposed　method,　especially　for　High　Definition　(HD)　and　Ultra-HD　(UHD)　video　sequences.