In　view　of　the　fact　that　the　traditional　laser　ultrasonic　imaging　test　takes　a　long　time　and　cannot　achieve　large　area　scanning　of　rail.　This　paper　explores　the　possibility　of　combing　the　laser-ultrasonic　technology　and　a　hybrid　intelligent　method　to　fast　achieve　classification　and　evaluation　of　artificial　rolling　contact　fatigue　(RCF)　defect　in　different　depths.　The　laser　ultrasonic　scanning　detection　system　is　used　to　collect　data　samples　from　different　locations　of　the　defects　quickly,　and　the　signals　are　detected　by　an　interferometer.　Once　the　characteristic　information　of　different　rail　defects　is　acquired　and　trained　by　Support　Vector　Machine　(SVM),　the　high　efficient　and　high-precision　rail　detection　can　be　realized　through　the　input　of　the　feature　in　the　detection　process.　The　hybrid　method　is　composed　by　Wavelet　Packet　Transform　(WPT),　Kernel　Principal　Component　Analysis　(KPCA)　and　SVM.　The　WPT　is　used　to　decompose　the　signal　of　surface　defect　in　different　frequency　bands.　The　KPCA　is　used　to　eliminate　the　redundancy　of　the　original　feature　set,　thereby　reducing　the　correlation　among　all　the　defect　features.　Wavelet　packet　time-frequency　coefficient　(X),　energy　(E)　and　local　entropy　(F)　are　generated　and　a　new　feature　(Ynew)　is　created　by　fusing　X,　E　and　F,　as　a　result　of　WPT　and　KPCA.　Finally,　a　support　vector　machine　(SVM)　method　is　used　to　classify　RCF　defect　in　different　depths.　It　implements　a　fast　classification　of　small　data.　Compared　with　single　features,　fusion　feature　has　the　highest　accuracy　rate　up　to　98.73%.　©　2018　Elsevier　GmbH