Nowadays,　Versatile　Video　Coding　(VVC)　has　achieved　a　superior　performance　than　previous　video　coding　standard　(High　Efficiency　Video　Coding).　The　Quadtree　with　Nested　Multi-Type　Tree　(QTMT)　coding　block　structure　can　enhance　the　coding　performance.　Nevertheless,　this　technique　also　leads　to　the　significantly　increasing　complexity　of　VVC　inter　coding.　Therefore,　complexity　optimization　is　an　urgent　problem　to　be　optimized　in　the　market　application　of　VVC.　To　solve　this　issue,　we　propose　a　Supervised-Contrastive-Learning-based　Inter　Partitioning　(SCLIP)　method　in　this　paper.　Firstly,　we　define　the　above　complexity　optimization　problem　as　a　supervised　classification　task.　Next,　we　develop　a　SCLIP　Estimation　Network　(SCLIPEst-Net)　with　a　supervised　contrastive　learning　module　and　a　classification　module.　After　training　on　a　newly　established　dataset,　the　SCLIPEst-Net　can　reasonably　predict　the　mode　partitioning.　Finally,　we　propose　an　overall　SCLIP　algorithm　that　effectively　determines　the　inter　partitions　of　VVC　with　a　low　computational　overhead.　Experimental　results　indicate　that　our　method　achieves　45.14%　average　Time　Saving　(TS)　with　a　2.40%　Bjøntegaard　Delta　Bit　Rate　(BDBR)　in　Random　Access　(RA),　outperforming　the　benchmarks.　©　2024　Elsevier　B.V.