Using hyperspectral analysis technology combined with pattern recognition, the classification and detection methods of eight potato micro seed potatoes(Daxiyang,Holland-14,Holland fifteen 041,Holland fifteen Q8,Jizhangshu 12,Jizhangshu 8,Xingjia 2 and Y2) were established.276 seed tuber samples were collected.The original spectra of 860-1 700 nm were preprocessed by standardize,11 points Savitzky-Golay smoothing and 4 points differential first derivative.Principal component analysis showed that the cumulative contribution rate of the first three principal components was 95.12%,including most information of the original spectra,and could be used as classification variables.Then,linear discriminant analysis,BP neural network and support vector machine were used for classification modeling.Finally,the classification models of 8 potato micro seed potatos were established by stratification and step by step.Firstly,the linear discriminant analysis model was used to distinguish Daxiyang,Holland-14,Holland fifteen 041 and other varieties.The average correct recognition rate was 88.79%.Then BP neural network model was established to divide the samples of other varieties into two categories:Jizhangshu 8,Y2,and Holland fifteen Q8,Jizhangshu 12,Xingjia 2,with an average correct recognition rate of 93.24%.Finally,the BP neural network model was used to distinguish Jizhangshu 8 and Y2,with the average correct recognition rate of 77.78%;and the support vector machine classification model was used to distinguish Holland fifteen Q8, Jizhangshu 12 and Xingjia 2,with the average correcct recognition rate of 8723%.The method was applied to the classification detection of eight potato seed potatos with the average correct recognition rate of 89.75%,which indicated that the hyperspectral analysis technology could be used for the classification and detection of potato micro seed potatos.