Light Field (LF) images captured under low illumination conditions typically exhibit low quality. Recent learning-based methods for low-light LF enhancement are generally tailored to specific illumination inputs, limiting their performance in real-world scenes. Moreover, how to maintain the inherent view-consistency in the enhanced images also remain as a difficult problem. In this paper, we propose to explore the view consistency for scene-adaptive low-light LF enhancement. We first analyze the view consistency for LF illumination maps and design a self-supervised view-consistent loss to keep the consistency between the illumination maps of different views in LFs. To enhance the model's perception of illumination, we combine both global and local information to estimate the illumination map, which is easily plugged into other models. Subsequently, we use the illumination maps to light up the low-light LF images and restore the corruption to produce the final enhanced image. Extensive experiments demonstrate that our View-Consistency Network (VCNet) outperforms state-of-the-art methods on real-world low-light LF datasets in both fixed lighting conditions and dynamic lighting conditions. Our proposed illumination adjustment is also demonstrated that can comprehensively improve the performance of existing methods in terms of both image quality and view consistency.