In order to identify wheat unsound kernel by hyperspectral image processing technology quickly and accurately, a detection method was researched based on spectral and image feature of wheat unsound kernel combined with multiclassification support vector machine. The hyperspectral images of wheat unsound kernels were collected and processed by image enhancement and threshold segmentation, which were used to extract 7 texture features and 5 morphological features as input to the classifier. The identification accuracy of model was established by multiclass support vector machine and then compared with different feature combination (spectral features, image features, spectral and image feature). The total identification rate of the 4 classification models based on spectral features was 94.73%, and the identification rate of black germ kernel and sound kernel was 100% and 98.63% respectively, but the identification rate of insectdamaged kernel and broken kernel were less than 90%. The recognition rate of unsound kernel based on image feature was relative lower. And when the spectral and image features were integrated, the recognition rate of the four class support vector machine model was 97.89%, the recognition rate of insectdamaged kernel was increased from 89.79% to 95.91%, and the recognition rate of broken kernel was increased from 84% to 94%. The results show that the hyperspectral image can quickly and nondestructively identify the unsound kernel of single grain wheat, which has potential application on rapid, highthrough and nondestructive detection of wheat seed quality.