Model-based polarimetric target decomposition (TD) generally solves scattering components and parameters under pre-set decomposition base, then decomposition features are also obtained. However, pre-set base could not be adjusted according to different scenes. Furthermore, solving the polarimetric parameters needs to explore additional information or consider limiting conditions to build equations, which is hard and easily to bring negative effects into decomposition features. To this end, we regard the TD as a process of learning decomposition base and features by deep learning. Then, the polarimetric decomposition feature learning (PDFL) model is proposed in this paper. Strictly, this model is not an incoherent TD method but a learning-based method. It dose not need to construct the parameter solution equations or fixed base. Then, the decomposition base and feature can be adaptively learned according to scattering characteristics of current dataset. Due to the characteristics of unsupervised reconstruction, deep auto encoder (DAE) is used as the model foundation. Then, some adjustments and constraints are utilized to make the DAE fit closely with TD. The encoder extracts latent vector from PolSAR data, then the decoder reconstructs pseudo data on this latent vector. The reconstruction can be regarded as the inverse process of TD, so the base matrix of decoder and the latent vector indicate the learned decomposition base and features when the model converges. The effectiveness of PDFL is verified on simulated and real PolSAR datasets. Compared with representative algorithms, proposed model gains more discriminative features and achieves competitive performance on terrain classification and segmentation tasks.