To address issues of insufficient illumination and complex degradation caused by underwater scattering in low-light underwater image enhancement tasks, we propose a Retinex-based variable attention framework. An illumination-guided variable attention module is designed, which uses semantic information from brightness feature maps to enhance dark regions and improve the adaptive enhancement capability of the model. We construct a spatial-frequency domain feature fusion module, which enhances the prominence of image texture information through multi-scale feature extraction and inter-level feature fusion. A medium transmission module is introduced to further address image artifacts caused by underwater scattering during enhancement. The experimental results show that compared with other cutting-edge methods, our method has significant improvements in four objective indicators, with PSNR reaching 22.781 8, SSIM reaching 0.882 1, UCIQE reaching 0.614 6, and UIQM reaching 3.365 9. The enhanced images exhibit excellent visual quality, validating the effectiveness of our algorithm in improving the clarity of low-light underwater images.