This　article　describes　how　fuzzy　support　vector　machines　(FSVMs)　function　well　with　good　anti-noise　performance,　which　receives　the　attention　of　many　experts.　However,　the　traditional　center-distance　fuzzy　weight　assignment　method　assigns　support　vectors　with　a　small　value　of　a　membership　degree　and　this　weakens　the　role　of　support　vectors　in　classification.　In　this　article,　a　piecewise　linear　fuzzy　weight　computing　method　is　proposed,　in　which　boundary　samples　are　assigned　with　a　larger　value　of　membership　degree　and　samples　far　from　the　mean　vector　are　assigned　a　smaller　value　of　membership　degree.　The　proposed　method　has　a　good　classification　performance,　because　the　influence　of　noise　samples　is　weakened　and　meanwhile　the　support　vectors　are　paid　much　more　attention.　The　experiments　on　the　UCI　database　and　MNIST　data　set　fully　verify　the　effectiveness　of　the　proposed　algorithm.　©　2018,　IGI　Global.