In　the　vector　operation　system,　each　data　operation　will　only　increase　or　decrease　the　same　number　for　each　component　of　the　vector,　that　is　to　say,　the　vector　is　regarded　as　a　data　object　on　the　whole,　and　each　operation　is　carried　out　on　the　whole.　For　example,　translation,　scaling　and　other　operations　in　computer　graphics.　Such　computing　systems　are　currently　widely　used　in　graphics,　machine　learning,　mathematical　modeling　and　other　fields.　When　the　data　in　the　vector　computing　system　is　homomorphic　encrypted　and　uploaded　to　the　cloud　server,　the　problem　of　low　vector　coding　efficiency　and　polynomial　coefficient　expansion　caused　by　a　large　number　of　polynomial　operations　is　generated.　The　existing　SIMD　coding　algorithm　encodes　an　n-dimensional　vector　integer　into　a　polynomial.　The　number　of　calculations　increases　exponentially　with　the　increase　of　n,　and　the　coding　efficiency　of　the　vector　is　very　low.　For　typical　FHE　schemes　such　as　BGV,　BFV　and　CKKS,　the　main　performance　bottleneck　comes　from　a　large　number　of　polynomial　algorithms.　Specifically,　encrypted　data　is　typically　composed　of　a　pair　of　polynomials　with　coefficients　of　hundreds　or　thousands　of　bits,　requiring　expensive　multiword　arithmetic.　In　addition,　large　polynomial　lengths　increase　the　computational　complexity.　Therefore,　in　this　paper,　a　vector　compression　coding　algorithm　based　on　SIMD(VCABSIMD)　is　proposed　to　solve　the　performance　problem　when　the　data　in　the　vector　computing　system　is　homomorphically　encrypted　and　uploaded　to　the　cloud　server　for　calculation.　The　vector　coding　efficiency　and　polynomial　calculation　are　deeply　optimized.　©　2023　SPIE.