To solve the problems of poor degradation awareness, easy detail loss, and ineffective color cast correction caused by existing underwater image enhancement algorithms, this study proposes a degradation-aware underwater image enhancement network based on wavelet transform. This model mainly contains a degradation representation extraction network based on contrastive learning and an underwater image enhancement network based on multiple-level wavelet transform. Firstly, the degradation representation extraction network uses an encoder and contrastive learning method to extract unique degradation representations from each underwater image. Secondly, a three-level wavelet transform module is built under the principle of multi-level wavelet transform enhancement algorithm, aiming to conduct multi-scale detail and color enhancement in the frequency domain. Lastly, a multiple-level wavelet transform enhancement network is built with three-level wavelet transform modules, and the extracted degradation representations are introduced into this network for better implementing multiple-level wavelet transform enhancement with perceived degradation information. Experimental results show that the proposed algorithm outperforms existing algorithms in color correction and detail enhancement in terms of sharply enhanced results, i.e. structural similarity is improved by 16%, peak signal-to-noise ratio is improved by 9%, and underwater image quality is improved by 14%, making it suitable for underwater image enhancement tasks.