Versatile　Video　Coding　(VVC)　is　a　modern　video　compression　standard　designed　to　efficiently　encode　high　definition　video　content,　regardless　of　its　diversity.　It　is　expected　to　deliver　superior　compression　performance　compared　to　the　previous　standard,　High　Efficiency　Video　Coding　(HEVC).　However,　the　bit　rate　control　problem　for　VVC　can　still　be　improved.　To　address　this　issue,　a　learning-based　initial　frame　Quantization　Parameter　(QP)　prediction　algorithm　has　been　proposed　in　this　paper.　This　algorithm　extracts　information　from　image　pixels　and　maps　it　to　a　feature　matrix　to　reduce　its　additional　cost.　Furthermore,　the　problem　of　inaccurate　determination　of　VVC　QPs　has　been　addressed　by　building　a　residual　network　to　represent　the　frame　complexity　progressively　and　learning　the　optimal　relationship　between　QPs　and　the　target　bit　rate.　Experimental　results　show　that　the　proposed　method　reduces　the　control　error　from　10.74%　to　7.19%　compared　to　the　original　encoder.