To　deal　with　the　problem　that　support　vector　machine　(SVM)　is　restricted　to　work　well　on　the　small　sample　sets,　based　on　the　Morlet　wavelet　kernel　function,　a　novel　reduced　support　vector　machine　on　Morlet　wavelet　kernel　function　(MWRSVM-DC)　is　proposed.　The　presented　algorithm　focuses　on　dealing　with　a　sample　set　through　density　clustering　prior　to　classifying　the　samples.　After　clustering　the　positive　samples　and　negative　samples,　the　algorithm　picks　out　such　samples　that　locate　on　the　edge　of　clusters　as　reduced　samples.　These　reduced　samples　are　treated　as　the　new　training　sample　set　used　in　SVM＇s　classifier　system.　Experiment　results　show　that　both　the　precision　and　the　efficiency　of　SVM＇s　are　improved　by　MWRSVM-DC.