In order to solve the problems of low contrast and high noise of low-light images a convolutional neural network enhancement model based on Retinex theory Retinex-RANet is proposed. It includes three parts the decomposition network the noise reduction network and the brightness adjustment network. The residual module RB and the jump connection were incorporated into the decomposition network and the features extracted by the first convolutional layer were fused with each RB extracted feature through the jump connection to ensure the complete extraction of the image features resulting in more accurate reflection and illumination components. The noise reduction network was based on the U-Net network and the cavity convolution and attention mechanism were added at the same time so as to extract more image-related information. The attention mechanism can better remove the noise in the reflected component and restore the details. The brightness adjustment network consists of a convolutional layer and a Sigmoid layer which is used to increase the contrast of the light components. Finally the reflection component after denoising by the noise reduction network and the light component after the brightness adjustment network were fused to obtain the final enhancement result. Experimental results show that Retinex-RANet not only improves the brightness of low-light images in subjective vision but also improves the color depth and contrast. In terms of objective evaluation indicators compared with R2RNet the PSNR value increased by 4. 4% and the SSIM value increased by 6. 1%. The results show that Retinex-RANet has better low-light image enhancement.