In　practical　engineering　problems,　due　to　the　influence　of　external　factors　such　as　artificial　or　environmental　disturbance,　there　will　inevitably　be　outliers　in　the　measurement　data　obtained　by　instruments.　The　outliers　will　deviate　from　the　measurement　data　in　some　way,　resulting　in　the　instability　of　the　accuracy　of　data　reconstruction.　For　the　measurement　data　with　outliers,　a　robust　reconstruction　method　based　on　Moving　Least　Squares　(MLS)　is　proposed.　This　method　fits　the　nodes　with　the　least　square　method　in　the　influence　domain.　The　abnormal　degree　of　generated　fitting　point　is　quantified　according　to　the　geometric　characteristic　parameter　α,　and　the　outliers　is　eliminated.　The　local　fitting　coefficients　are　determined　with　the　weight　least　square　by　using　the　remaining　nodes　in　the　influence　domain,　and　the　curve　and　surface　reconstruction　is　completed　by　moving　the　influence　domain.　By　trimming　only　one　point　in　each　influence　domain,　the　multiple　outliers　of　measurement　data　can　be　effectively　handled,　and　it　is　unnecessary　to　set　threshold　values　subjectively　or　assign　weights,　which　avoids　the　negative　influence　of　manual　operations.　The　numerical　simulation　and　experimental　results　show　that　the　proposed　method　can　effectively　eliminate　the　outliers　in　the　measurement　data.　Compared　with　the　MLS　method,　the　accuracy　of　the　numerical　case　can　be　improved　by　more　than　60%.　©　2021,　Jilin　University　Press.　All　right　reserved.