In this paper, the polarimetric synthetic aperture radar (PolSAR) scene classification is solved by using a novel low -rank tensor-based multi-view subspace representation (LRT-MSR) method. PolSAR data can be described in multimodal feature spaces, such as PolSAR coherent/covariance/scattering matrices, or the various polarimetric decompositions. Different pseudo-color images from multiple spaces provide enough visual information for making a comprehensive representation. Our method applies a low-rank tensor-based subspace clustering way to explore the information from multi-view pseudo-color images. Tensor, as the high order matrix, is used to capture the correlations of underlying multi-view data. Furthermore, the method is constrained by a low-rank term that elegantly models the cross information from different views, and achieves a series of representation matrices from the redundant information. Finally, a spectral cluster method is used to make the final classification. The experimental results on PolSAR image dataset show the effectiveness of the applied method.