Recently,　the　automatic　diagnosis　of　Turner　syndrome　(TS)　has　been　paid　more　attention.　However,　existing　methods　relied　on　handcrafted　image　features.　Therefore,　we　propose　a　TS　classification　method　using　unsupervised　feature　learning.　Specifically,　first,　the　TS　facial　images　are　preprocessed　including　aligning　faces,　facial　area　recognition　and　processing　of　image　intensities.　Second,　pre-trained　convolution　filters　are　obtained　by　K-means　based　on　image　patches　from　TS　facial　images,　which　are　used　in　a　convolutional　neural　network　(CNN);　then,　multiple　recursive　neural　networks　are　applied　to　process　the　feature　maps　from　the　CNN　to　generate　image　features.　Finally,　with　the　extracted　features,　support　vector　machine　is　trained　to　classify　TS　facial　images.　The　results　demonstrate　the　proposed　method　is　more　effective　for　the　classification　of　TS　facial　images,　which　achieves　the　highest　accuracy　of　84.95%.