This　paper　investigates　a　lightweight　Vision　Transformer　(Vi　T)　model　based　on　Support　Vector　Machine　(SVM)　optimization,　using　methods　such　as　structural　optimization　and　hyperparameter　optimization　that　introduce　SVM　into　the　ViT　model,　with　the　aim　of　improving　the　model＇s　performance　in　target　detection　tasks.　This　study　addresses　several　aspects　of　the　lightweight　ViT　model　structure　design,　SVM　optimization　strategy,　and　analysis　of　the　total　optimization　process.　First,　SVM　is　introduced　into　the　structural　design　of　the　Vi　T　model　to　optimize　the　feature　extraction　process.　Second,　by　optimizing　the　hyperparameters　of　the　ViT　model,　the　training　process　of　the　model　is　accelerated　with　the　powerful　classification　ability　of　SVM.　Finally,　the　overall　optimization　process　is　analyzed　and　experimentally　validated　to　demonstrate　the　superior　performance　of　the　optimized　model　in　the　target　detection　task　on　standard　datasets　such　as　the　COCO　dataset.　The　introduction　of　Support　Vector　Machines　(SVMs)　into　the　Vision　Transformer　(ViT)　model　allows　the　model　parameters　to　be　effectively　reduced　in　resource-constrained　scenarios,　improves　the　training　and　inference　speeds,　and　significantly　improves　the　overall　performance　of　the　model.　©　2024　IEEE.