The invention discloses a multilinear ICA (independent component analysis)-based spectrum tensor dimension reduction classification method. In the method, factors affecting spectrum characteristics of a terrain are used as within-class factors, a within-class factor, a class and a pixel spectrum serve as a mode respectively to build a three-order tensor, and dimension reduction based on low rank tensor decomposition is carried out on the three-order tensor; the three-order tensor D is subjected to multilinear ICA decomposition, and a class space matrix Cclass and a within-class factor space matrix Cwithin-class are obtained; and a supervised classifier is adopted to classify classless test hyperspectral images d. After the model is built, the hyperspectral images can be classified, no adjustment is needed, and according to other tensor modeling methods, the best classification effects are achieved only by repeated setting and parameter adjustment. All pixel spectrums of the same class are mapped to the same coefficient vector, influences from various factors are reduced to the minimum, the classification precision is improved, and the result is stable. When unknown pixel spectrums are classified, which factor affects the spectrums can be inferred.