To address problems caused by deep-sea imaging， such as color distortion， low contrast， and blurred structures in underwater images， an underwater image enhancement algorithm based on multi-scale perception and multi-dimensional space fusion was proposed. By combining spatial， channel， and three-dimensional features， image information was transmitted in parallel by the algorithm to a multi-dimensional feature extraction network and an encoder. Firstly， a multiscale feature refinement module was introduced into the feature extraction network to further process the extracted feature information， allowing the network to learn information at different scales more accurately. Secondly， a multidimensional color enhancement module was incorporated into the encoder to enhance image details and colors. Finally， an adaptive enhancement network was designed to further process the feature information and fuse multi-level features， then the decoder was used to generate the final enhanced image. Experimental results on public datasets demonstrate the outstanding performance of the proposed algorithm. Specifically， it achieves a Peak Signal-to-Noise Ratio （PSNR） of up to 24.865 1 dB and a Structural Similarity （SSIM） of 0.895 4， representing improvements of 1.580 6 dB and 0.039 8 over Hybrid Fusion Method （HFM）， respectively， and it has the Underwater Color Image Quality Evaluation （UCIQE） and Underwater Image Quality Measure （UIQM） up to 0.593 1 and 3.102 8， respectively， surpassing HFM by 0.038 4 and 0.151 4， respectively. It can be seen that the proposed algorithm improves underwater visual quality effectively.