The present paper put forward a non-destructive detection method which combines semi-transmission hyperspectral imaging technology with manifold learning dimension reduction algorithm and least squares support vector machine (LSSVM) to recognize internal and external defects in potatoes simultaneously.Three hundred fifteen potatoes were bought in farmers market as research object,and semi-transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images of normal external defects (bud and green rind) and internal defect (hollow heart) potatoes.In order to conform to the actual production,defect part is randomly put right,side and back to the acquisition probe when the hyperspectral images of external defects potatoes are acquired.The average spectrums (390~1 040 nm) were extracted from the region of interests for spectral preprocessing.Then three kinds of manifold learning algorithm were respectively utilized to reduce the dimension of spectrum data,including supervised locally linear embedding (SLLE),locally linear embedding (LLE) and isometric mapping (ISOMAP),the low-dimensional data gotten by manifold learning algorithms is used as model input,Error Correcting Output Code (ECOC) and LSSVM were combined to develop the multi-target classification model.By comparing and analyzing results of the three models,we concluded that SLLE is the optimal manifold learning dimension reduction algorithm,and the SLLE-LSSVM model is determined to get the best recognition rate for recognizing internal and external defects potatoes.For test set data,the single recognition rate of normal,bud,green rind and hollow heart potato reached 96.83%,86.96%,86.96% and 95% respectively,and he hybrid recognition rate was 93.02%.The results indicate that combining the semi-transmission hyperspectral imaging technology with SLLE-LSSVM is a feasible qualitative analytical method which can simultaneously recognize the internal and external defects potatoes and also provide technical reference for rapid on-line non-destructive detecting of the internal and external defects potatoes.