Aiming at the problem of low brightness and blurred detail information in low-light images, this paper proposes a context-aware low-light image enhancement algorithm. First, the context-aware module for extracting detail information and edge artifacts was investigated. Nonlinear mapping was performed using activation functions to get the importance of features in the current context. Second, the model used linear attention gating mechanism instead of the multi-head attention module in Transformer. It reduced the computational complexity in high-resolution images while maintaining the performance. Finally, the reconstruction guidance module was designed to focus on the information in the low-light region during image reconstruction. The correlation information between the positions in the input sequence was captured to improve the expressiveness of the model for the reconstruction processing task. The results show that compared with the existing typical low-light enhancement algorithm URetinex, the PSNR and SSIM of images generated on the dataset LOL are increased by 1.33% and 3.73%, and the PSNR and SSIM of images generated on the dataset SICE are increased by 1.2% and 2.8%. The proposed algorithm can effectively enhance low-light images and generate clear and high-fidelity images.