In　dealing　with　a　large　number　of　train　samples,　Support　Vector　Regression　(SVR)　algorithm　is　slow.　In　particular,　while　new　sample　is　added,　all　the　training　samples　must　be　re-trained.　In　this　paper,　a　new　SVR　incremental　algorithm　is　presented,　which　is　based　on　boundary　vector.　The　algorithm　takes　full　advantages　of　the　geometric　information　of　training　sample　sets.　The　observed　data　of　China＇s　GDP　is　used　as　a　case　study　for　the　new　algorithm.　The　computing　results　show　that　the　new　algorithm　not　only　can　guarantee　the　accuracy　of　machine　learning　and　good　generalization　ability,　but　also　can　increase　the　learning　speed　of　the　algorithm　than　the　classical　SVR　algorithm,　and　can　be　used　rapid　incremental　learning.　©2010　IEEE.