Supervised enhancement models trained on synthetic datasets struggle to accurately simulate the physical mechanisms of underwater imaging,limiting their adaptability to complex real-world scenarios.Unsupervised models,often based on cycle consistency frameworks,tend to suffer from excessive structural complexity and low efficiency.To address these issues,this paper proposes an unsupervised rapid underwater image enhancement algorithm based on contrastive learning,achieving efficient enhancement through unidirectional style transfer from the turbid domain to the clear domain.The algorithm employs a hourglass bottleneck structure with multi-feature selection (MFS) and subpixel convolutional fusion (SCF) to build the generator,ensuring high feature extraction performance while reducing complexity and accelerating inference speed.An optimized contrastive loss with improved positive and negative sampling strategies enhances the model′s adaptability to diverse underwater datasets.Experimental results demonstrate that the proposed algorithm improves PSNR and UIQM by 6.5% and 5.3%,respectively,shortens inference time to 0.04 seconds,and decreases GPU memory by at least 65%,achieving a strong balance between performance and complexity.