Hyperspectral images of wheat can identify seeds quickly, accurately, and nondestructively. However, most of the existing hyperspectral classification methods only use spectral information but ignore spatial information, resulting in unsatisfactory classification performance. To address these issues, we propose a spatial-spectral feature extraction method to identify seeds. Specifically, we first fuse the spatial and spectral features and then perform denoising. Subsequently, the principal component analysis is employed to extract features from the spatial-spectral data. Ultimately, the support vector machine trains and optimizes the model. Experimental results demonstrate that our method has the highest classification accuracy compared with the state-of-the-art methods. The classification accuracy of our method is achieved at 97.64% on the whole dataset. In addition, our method achieves better classification performance for small sample data.