For　least　squares　support　vector　machine　(LS-SVM)　classifier　to　the　loss　of　sparseness　and　generalization,　a　pruning　modeling　method　is　proposed　based　on　Quadratic　Renyi　entropy.　The　kernel　principal　component　is　adopted　for　data　pre-processing,　and　the　training　set　is　divided　randomly.　Then　the　concept　of　quadratic　Renyi　entropy　is　introduced　as　the　basis　of　training　and　pruning　in　LS-SVM　classifier.　UCI　typical　datasets　of　classification　are　used　for　testing　the　performance　of　this　new　model.　Experimental　results　show　that　the　new　algorithm　takes　full　account　the　location　of　the　Lagrange　multiplier,　thus　the　sparseness　and　generalization　ability　of　the　classifier　can　be　improved.　©　2012　IEEE.