Versatile　Video　Coding　(VVC)　introduces　various　advanced　coding　techniques　and　tools,　such　as　QuadTree　with　nested　Multi-type　Tree　(QTMT)　partition　structure,　and　outperforms　High　Efficiency　Video　Coding　(HEVC)　in　terms　of　coding　performance.　However,　the　improvement　of　coding　performance　leads　to　an　increase　in　coding　complexity.　In　this　paper,　we　propose　a　multi-feature　fusion　framework　that　integrates　the　rate-distortion-complexity　optimization　theory　with　deep　learning　techniques　to　reduce　the　complexity　of　QTMT　partition　for　VVC　inter-prediction.　Firstly,　the　proposed　framework　extracts　features　of　luminance,　motion,　residuals,　and　quantization　information　from　video　frames　and　then　performs　feature　fusion　through　a　convolutional　neural　network　to　predict　the　minimum　partition　size　of　Coding　Units　(CUs).　Next,　a　novel　rate-distortion-complexity　loss　function　is　designed　to　balance　computational　complexity　and　compression　performance.　Then,　through　this　loss　function,　we　can　adjust　various　distributions　of　rate-distortion-complexity　costs.　This　adjustment　impacts　the　prediction　bias　of　the　network　and　sets　constraints　on　different　block　partition　sizes　to　facilitate　complexity　adjustment.　Compared　to　anchor　VTM-13.0,　the　proposed　method　saves　the　encoding　time　by　10.14%　to　56.62%,　with　BDBR　increase　confined　to　a　range　of　0.31%　to　6.70%.　The　proposed　method　achieves　a　broader　range　of　complexity　adjustments　while　ensuring　coding　performance,　surpassing　both　traditional　methods　and　deep　learning-based　methods.　©　The　Author(s),　under　exclusive　licence　to　Springer-Verlag　GmbH　Germany,　part　of　Springer　Nature　2024.