The invention discloses a PoLSAR image classification method based on FCN and sparse-low rank subspace expression integration; the method fully utilizes the powerful ability of the FCN which can automatically learn non-linear deep multiple dimensional space characteristics from PoLsar data, can utilize the advantages that a linear dimension reduction algorithm based on sparse-low rank map embedment can simultaneously capture local and global structure information of the PoLsar data, and can effectively integrate the FCN and sparse-low rank subspace expression, thus solving the PoLSAR image non-linear deep space feature extraction problem, the dimension reduction problem, the valid integration problem between polarized information and space information, and effectively solving the PoLSAR image classification problem. The method can obtain integrated multi-layer subspace characteristic information containing various types; the information contains linear and non-linear types, shallow anddeep types, local and global types, and polarized and multiple dimensioned space types, thus providing strong discrimination ability, and greatly improving PoLSAR image classification accuracy.