Wheat is a major food crop and occupies an important position in Chinese agricultural production, transportation, and food processing. Unsound kernel seriously affects wheat quality and food security. Wheat unsound kernel is mainly produced during production, storage, and packaging. At present, the manual sorting method is the main method for detecting wheat kernel quality in China. It is subjective, time-consuming, laborious, and costly. Therefore, the rapid and accurate identification method of the wheat unsound kernel will increase productivity and ensure food security. So the method for rapid and accurate detection of wheat unsound kernel was proposed by using hyperspectral image technology and the method of characteristic band selection. In this paper, near-infrared hyperspectral imaging system was used to collected hyperspectral reflection image of 1 000 wheat kernels (including healthy kernels, sprouted kernels, mildewed kernels, and kernels infected with Fusarium head blight, their respective amount are 250) at 862.9~1 704.2 nm (a total of 256 bands) and the average reflectivity of each sample were extracted from region of interests of hyperspectral images as classification characteristics. This paper conducts pre-processing for the extracted full-wave bands spectral information through window smoothing, first order derivative and vector normalization. It will also amplify hidden signals of the original spectral data and erase random errors. On the basis of pre-processing, feature extraction by applying discriminant partial least squares (DPLS) and orthogonal linear discriminant analysis (OLDA) to lower the redundancies of the data. Finally, it establishes identification model for 4 kinds of wheat through pattern recognition (BPR). The experiment results showed that the average identification accuracy of the model for wheat unsound kernel established by using full-wave bands spectral information is 97.8%. The analysis also proves that it is feasible to detect wheat unsound kernel by using near-infrared hyperspectral imaging technology. Though full-wave bands spectral information achieved better detection effect, the high costs of hyperspectral imaging equipment and large amount of hyperspectral full-wave bands spectral information data fail to meet the high requirement of calculation for site equipment. Therefore, this paper uses successive projections algorithm (SPA) to select characteristic bands among full-wave bands data and lower the number of band from 256 dimensions to 10 dimensions to improve the operation and calculation speed of the system. So 10 characteristic bands were taken to establish identification model for wheat unsound kernel. The experiment results showed that the average identification accuracy of the 10 characteristic bands is only 83.2%, which means that though the 10 characteristic bands improve the real-time capability of the system, but they show worse identification accuracy. In order to achieve the identification effect that is basically equivalent to the characteristics of the whole band, this paper uses the combination of spectral features and image features to establish identification model of the wheat unsound kernel. All kinds of relevant information (morphological information, texture information, spectral information) of the kernel of the above 10 selected wave bands are integrated. The experimental results showed that the combination of spectral information and image information in 10 characteristic bands can achieve an average identification accuracy of 94.2%. Its identification effect is basically consistent with the use of full-wave bands spectral data. This paper uses hyperspectral imaging system to explore the feasibility of wheat unsound kernel detection. From the analysis of the above experiment, it can be seen that near-infrared hyperspectral imaging technology shows better results in the detection of wheat unsound kernel. It can guarantee the identification accuracy of the system while improving calculation speed so it offers an effective research orientation for later development of equipment that is able to detect wheat unsound kernel in a quick way.