Intelligent　fault　diagnosis　of　wind　turbine　rolling　bearings　is　an　important　task　to　improve　the　reliability　of　wind　turbines　and　reduce　maintenance　costs.　In　this　paper,　a　novel　intelligent　fault　diagnosis　method　is　proposed　for　wind　turbine　rolling　bearings　based　on　Mahalanobis　Semi-supervised　Mapping　(MSSM)　manifold　learning　algorithm　and　Beetle　Antennae　Search　based　Support　Vector　Machine　(BAS-SVM),　mainly　including　three　stages　(i.e.,　feature　extraction,　dimensionality　reduction,　and　pattern　recognition).　In　the　first　stage,　Multiscale　Permutation　Entropy　(MPE)　is　utilized　to　extract　the　feature　information　from　rolling　bearing　vibration　signals　at　multiple　scales,　while　a　high-dimensional　feature　set　is　constructed.　Second,　the　proposed　MSSM　algorithm,　combining　the　advantages　of　Mahalanobis　distance,　semi-supervised　learning　and　manifold　learning,　is　applied　to　reduce　the　dimension　of　high-dimensional　MPE　feature　set.　Subsequently,　low-dimensional　features　are　input　to　the　BAS-SVM　classifier　for　pattern　recognition　using　the　BAS　algorithm　to　search　the　best　parameters.　The　performance　of　the　proposed　fault　diagnosis　method　was　confirmed　by　conducting　a　fault　diagnosis　experiment　of　wind　turbine　rolling　bearings.　The　application　results　show　that　the　proposed　method　can　effectively　and　accurately　identify　different　states　of　wind　turbine　rolling　bearings　with　a　recognition　accuracy　of　100%.　©　2020　Elsevier　Ltd