Deep learning-based algorithms for enhancing underwater images have demonstrated outstanding performance in recent years. However, numerous limitations exist in applying existing methods to various underwater environments, viewed as water types. The lack of generalization ability and visual consistency remain significant challenges, as they neglect the domain gaps of underwater scenes and the differences in the multi-frequency nature of information. A critical desideratum in underwater image enhancement is to establish a strong correspondence between the learned features and water types while also exploring multi-frequency analysis. In response to these issues, we propose a novel domain adaptive multi-frequency underwater image enhancement network. This network leverages the capability of adapting feature learning to varying water types while also supporting a simultaneous feature decode for high and low frequencies. Specifically, we first construct a module for domain adaptation with a water type classifier to distinguish the impacts of different water types, which naturally reveals domain adaptation between water types. This enhances the model’s sensitivity to water types by generating the domain-sensitive feature. In addition, we design a dual-branch decoder with self-attention to decompose the domain-sensitive features into sub-bands of high and low frequencies. Each branch contains distinct semantic content aimed at promoting multi-frequency feature complementarity. This helps the model to accurately capture local detail and global context, thereby further improving image quality. Finally, we design a multi-stage training strategy equipped with multiple loss functions to improve the stability of the model. The proposed method can adaptively deal with degraded images of different water types and enhance underwater images with high quality by processing high-frequency and low-frequency components synchronously. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art methods in terms of both visual quality and evaluation metrics.