The invention discloses a Tucker decomposition-based spectral tensor dimension reduction and classification method. The method comprises the steps of constructing a three-order tensor by taking factors influencing a spectral feature of a ground object as intra-class factors and taking the intra-class factors, classes and pixel spectrums as modes respectively, and performing low-rank tensor decomposition-based dimension reduction on the three-order tensor; performing low-rank tensor decomposition on the three-order tensor to obtain a nuclear tensor Z, a class space matrix Uclass, an intra-class factor space matrix Uwithin-class and a pixel spectral matrix Upixels; and performing classification on unclassified test hyperspectral images d by adopting a supervised classifier. According to the method, the hyperspectral images can be classified after model building, and adjustment does not need to be carried out; for other tensor modeling methods, the best classification effect can be achieved by repeated setting and adjustment of parameters; and all pixel spectrums in a class are mapped to a same coefficient vector, so that the influence of various factors is reduced to minimum, the classification precision is improved, and a result is stable.