To improve the clarity of low-light images and avoid color distortion, a low-light image enhancement algorithm based on the attention mechanism and convolutional neural network (CNN) is proposed to improve image quality. First, the training data is synthesized based on the Retinex model, and the original image is transformed from RGB (red-green-blue) color space to HSI (hue-saturation-intensity) color space. Then, an A-Unet model is constructed to enhance the brightness component by combining the attention mechanism and CNN. Finally, the enhanced image is obtained by transforming images from the HSI color space to the RGB color space. Experimental results show that the proposed algorithm can effectively improve the image quality, improve the image clarity, and avoid the color distortion. Good results can be obtained in the experiments of synthesizing low-light images and real low-light images, and the subjective and objective evaluation indexes are better than that of the comparison algorithm.