Versatile　video　coding　(VVC)　has　made　significant　progress　in　the　compression　efficiency　of　video　coding.　It　achieves　better　performance　and　halves　bitrate　compared　to　the　high　efficiency　video　coding　(HEVC)　under　the　same　visual　quality.　However,　the　complexity　of　VVC　has　also　been　significantly　increased,　especially　in　inter　prediction.　This　paper　proposes　an　improved　motion　vector　prediction　method　based　on　neural　networks　for　motion　estimation　(ME).　Firstly,　dynamic　weights　are　proposed　in　the　process　of　selecting　the　best　MVP　for　advanced　motion　vector　prediction　(AMVP);　secondly,　we　build　the　motion　vector　prediction　model　based　on　the　deep　neural　network;　finally,　the　model　is　embedded　in　VVC　to　acquire　a　more　accurate　MVP　and　reduce　the　encoding　complexity　of　ME.　Experimental　results　show　that　the　proposed　algorithm　can　reduce　the　encoding　time　of　motion　estimation　under　the　premise　of　guaranteeing　video　quality.　©　2021　IEEE.