Deep learning methods excel in Polarimetric SAR (PolSAR) image classification. However, existing methods typically sample an image block for each pixel with a fixed-size square window, which always contains inconsistent/incomplete content with the central pixel, resulting in many misclassifications especially in boundary and heterogeneous regions. So, a size-fixed square window is not enough for representing various terrain objects. To address this issue, we develop a content-adaptive multi-region deep network to obtain contextual consistent sampling windows for diverse terrain objects. Firstly, a complex scene of PolSAR image is partitioned into homogeneous, heterogeneous and boundary regions. Then, sampling windows with adaptive direction and scale are designed for three distinct regions. Besides, windows with central and global regions are proposed to provide additional local and global information. Finally, a fusion network is designed to adaptively combine different sampling windows to enhance classification performance. Experimental results on three real data sets demonstrate that the proposed method can achieve superior performance in both edge details and heterogeneous terrain objects compared with the state-of-the-art methods.