To address issues such as detail loss and inconsistent results across different brightness regions in the Zero-DCE network, an unsupervised low-light image enhancement algorithm based on the enhanced depth curve estimation network (EnDCE-Net) is proposed. This algorithm explores the potential mapping relationship between low-light images and unpaired normal-light images to achieve significant improvements in image quality under low-light conditions. First, a novel feature extraction network is introduced, which integrates multiple skip connections and convolutional layers, allowing for the effective fusion of low-level and high-level features. This enables the network to learn the key features of low-light images and enhances its ability to process them. Second, a set of joint no-reference loss functions is designed, emphasizing brightness-related features during the optimization process, which facilitates more efficient parameter updates and enhances the overall quality and effectiveness of the image enhancement. To evaluate the effectiveness of the proposed algorithm, comparative experiments were conducted on five publicly available datasets. Compared to the suboptimal algorithm Zero-DCE, the PSNR and SSIM on the reference dataset SICE were improved by 9.4% and 21%, respectively. On the no-reference datasets LIME, DICM, MEF, and NPE, the NIQE scores reached 4.04, 3.04, 3.35, and 3.83, respectively. The experimental results demonstrate that the proposed algorithm outperforms others, producing enhanced images with natural colors, balanced brightness, and clear details. Both subjective visual assessments and objective quantitative metrics show significant improvements over the competing algorithms, highlighting the excellence and advancement of the proposed method in image enhancement.