A　novel　reduced　support　vector　machine　on　Morlet　wavelet　kernel　function　(MWRSVM-DC)　is　proposed,　which　combined　the　Morlet　wavelet　kernel　function　with　the　reduced　support　vector　machine　(SVM)　model　properly.　Based　on　the　wavelet　decomposition　and　conditions　of　the　support　vector　kernel　function,　Morlet　wavelet　kernel　function　for　support　vector　machine　(SVM)　is　proposed.　At　the　same　time,　because　SVM　is　restricted　to　work　well　on　the　small　sample　sets,　a　novel　reduced　SVM　on　density　clustering　(RSVM-DC)　is　proposed.　This　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　not　only　the　precision　but　also　the　efficiency　of　SVM＇s　are　improved　by　MWRSVM-DC.