For　the　loss　of　sparseness　in　least　squares　support　vector　machine　(LS-SVM)　model,　a　new　pruning　algorithm　using　Renyi　entropy　for　LS-SVM　modeling　is　presented.　The　kernel　principal　component　is　adopted　for　data　pre-processing,　then　the　training　subsets　are　divided　randomly.　To　solve　the　problem　that　the　conventional　pruning　algorithm　cannot　take　full　account　the　location　of　the　Lagrange　multiplier,　the　concept　of　quadratic　Renyi　entropy　is　introduced　as　the　basis　of　training　and　pruning　in　LS-SVM　modeling.　The　results　of　simulation　verify　the　validity　of　the　algorithms,　thus　the　sparseness　and　generalization　ability　of　the　model　can　be　improved.　The　presented　algorithm　can　be　applied　to　multiple-output　modeling.　©　2012　IEEE.