Polarimetric synthetic aperture radar (PolSAR) image classification is a challenging task due to lack of effective feature representation approaches. However, when it comes to deep feature representation, there is little research that pays enough attention to make full use of existing expert knowledge. We propose a model for deep polarimetric feature extraction, and a superpixel map is used to integrate contextual information. The proposed model uses multiple polarimetric algebra operations, polarimetric target decomposition methods, and convolutional neural network (CNN) to extract deep polarimetric features. The core idea is to utilize expert knowledge of the target scattering mechanism interpretation to assist the CNN classifier in feature extraction and employ superpixel algorithm based on Wishart distribution to improve the final classification performance. The proposed method is able to get abstract and discriminative representation from initial features, achieving robust and improved performance for PolSAR images. Compared with other state-of-the-art methods, experiments on the classification of three real PolSAR datasets are performed to demonstrate the superiority of the proposed approach.