The images generated by low-light image enhancement algorithms based on deep learning generally have problems such as noise highlighting and detail loss. However, the performance of end-to-end deep learning algorithms largely depends on the extraction ability of the backbone network. Therefore, exploring more effective backbone network structures can improve the performance benefits of low-light enhancement tasks. This study proposes an image enhancement algorithm based on a composite backbone network fusion strategy, which integrates backbone networks from different image enhancement algorithms to improve the overall network’s feature extraction ability. The algorithm integrates feature information from different backbone networks layer by layer and guides composite features into the decoder. It then fully utilizes different upsampling methods to stack the fused features of the backbone network, ultimately generating images under normal lighting conditions. Through quantitative and qualitative comparative experiments with existing mainstream algorithms, the results show that our method significantly improves the brightness of low-light images while preserving the detailed features of the images. In terms of objective indicators such as peak signal-to-noise ratio and structural similarity, it achieves 24.35 dB and 0.871 in the LOL-V2 dataset, effectively solving the problems of noise highlighting and detail loss after image enhancement.