Low-light images have low brightness， low contrast， and color distortion， and most existing enhancement algorithms do not deal with different channels differently， which is not conducive to the extraction of multi-level features. Therefore， this study proposes a low-light image enhancement algorithm based on a multi-channel fusion attention network. Firstly， we introduced octave convolution （OctConv） into the residual structure after channel splitting and propose a multi-level feature extraction module. Secondly， we proposed a cross-scale feature attention module using an attention mechanism and cross-residual structure. Thirdly， we obtained multi-level information by stacking modules with different sizes and channels. Finally， we performed feature fusion in the channel dimension and obtained the final output through the reconstruction module. The experimental results showed that compared with the RISSNet algorithm， the peak signal-to-noise ratio and structural similarity of real images were improved from 27.001 6 dB and 0.889 2 to 27.978 1 dB and 0.925 5， respectively. The proposed algorithm achieved the best results in four objective evaluation indicators： peak signal-to-noise ratio， structural similarity， mean squared error， and visual information fidelity. The algorithm can effectively improve the brightness and contrast of low-light images with well-maintained image textures and colors.