A Retinex-based low-light image enhancement algorithm based on residual and multi-scale attention mechanisms was proposed to address the issues of unclear texture enhancement and the limitation of single light intensity in traditional Retinex algorithms. Initially, a U-Net structure with residual modules and skip connections was utilized to ensure that the model can fully extract feature information and accurately decompose the original image into illumination and reflectance components. Subsequently, the recovery network combined with the multi-scale attention module was applied to process the reflectance component, enhancing the network's perception of degraded information and its ability to capture texture detailed information. Then, the illumination component's light intensity was enhanced by the adjustment network. Finally, the processed reflectance component and illumination component were fused to obtain the final output. Comparative subjective and objective evaluations with seven similar algorithms indicated that the enhancement results achieved by the proposed algorithm exhibit excellent performance in contrast enhancement, noise reduction, and natural color rendering. The experimental results demonstrate that the method presented in this paper effectively improves image contrast, suppresses noise, and provides a visually appealing presentation that aligns with human visual perception. Moreover, it is effective in various lighting environments, offering a reliable source of information for subsequent image processing.