Band selection is a fundamental problem in hyperspectral data processing. In this paper, we present a semi-supervised learning approach and a hypergraph model to select useful bands based on few labeled object information. The contributions of this paper are two-fold. Firstly, the hypergraph model captures multiple relationships between hyperspectral image samples. Secondly, the semi-supervised learning method not only utilizes unlabeled samples in the learning process to improve model performance, but also requires little labeled samples which can significantly reduce large amount of human labor and costs. The proposed approach is evaluated on AVIRIS and APHI datasets, which demonstrate its advantages over several other band selection methods.