It is known that hyperspectral image (HSI) classification is a high-dimension low-sample-size problem. To ease this problem, one natural idea is to take the feature extraction as a preprocessing. A graph embedding model is a classic family of feature extraction methods, which preserves certain statistical or geometric properties of the data set. However, the graph embedding model considers only the pairwise relationship between two vertices, which cannot represent the complex relationships of the data. Utilizing the spatial structure of HSI, in this letter, we propose a spatial hypergraph embedding model for feature extraction. Experimental results demonstrate that our method outperforms many existing feature extract methods for HSI classification.