The　monitoring　of　the　status　of　the　wind　turbine　blades　is　significant　for　the　wind　generation　system　and　currently　mainly　dependent　on　manual　visual　inspections.　The　variance　of　the　blade　defects　and　the　lack　of　the　blade　defect　images　make　the　defect　identification　of　the　wind　turbine　blades　challenging.　This　paper　proposes　a　defect　identification　method　of　wind　turbine　blades　based　on　defect　semantic　features　with　transfer　feature　extractor.　A　deep　convolutional　neural　network　(DCNN)　is　built　and　is　trained　on　the　ImageNet　Large　Scale　Visual　Recognition　Challenge　dataset.　The　deep　hierarchical　features　of　the　training　blade　images　are　extracted　by　the　trained　DCNN　and　fed　into　a　classifier.　By　training　on　the　labeled　blade　images,　the　first　n　layers　of　the　trained　DCNN　is　selected　as　the　transfer　feature　extractor　to　extract　the　defect　semantic　features　and　the　defect　classifier　is　also　obtained.　The　blade　images　can　be　diagnosed　by　the　defect　classifier　based　on　the　defect　semantic　features.　The　experiments　are　conducted　on　a　real　dataset　of　wind　turbine　blade　images.　The　experimental　results　demonstrate　the　high　learning　ability　of　the　proposed　method　from　the　small　samples　and　its　effectiveness　for　the　defect　identification　of　wind　turbine　blades.　(C)　2019　Elsevier　B.V.　All　rights　reserved.