The invention discloses a high-spectrum image classification method based on a combined loss enhanced network, and the method is characterized in that the method comprises the following steps: 1), PCA dimension reduction; 2), spatial domain block extraction; 3), coding path feature extraction; 4), classification task training target building; 5), decoding path feature extraction and reconstruction; 6), network combined training; 7), high-spectral testing classification. The method can achieve the combination of learning reconstruction loss and classification discrimination loss functions in the same network structure in a mode of end-to-end training, so the spatial spectrum information of a high-spectral image is used efficiently, and the learning of CNN for unimportant feature variables is automatically weakened, so as to reduce the complexity of a high-spectral classification model. Meanwhile, the dependence of a high-spectrum image classification method on a label sample is reduced, and the classification precision is improved.