In view of the low image brightness in low-light scenarios, an unsupervised low-light image enhancement algorithm has thus been proposed based on a lightweight neural network. A learnable content-adaptive S-shaped brightness mapping curve is introduced to expand the brightness adjustment range and maintain good contrast while ensuring brightness. A lightweight brightness curve estimation network is designed, which adopts unsupervised training to learn the mapping relationship between the input image and the fitted curve, thus solving the problem of difficult access to labeled data. Experimental results show that the proposed lightweight image enhancement network is characterized with a low computational cost so as to effectively reduce computation time, and achieve a good performance on different datasets, thus providing an effective solution for image enhancement in low light scenarios.