Photos taken in low-light conditions will suffer from a series of visual problems due to underexposure， such as low brightness， loss of information， noise and color distortion. In order to solve the above problems， a dual-branch residual low-light image enhancement network combined with attention was proposed. Firstly， the improved InceptionV2 was used to extract shallow features. Secondly， Residual Feature extraction Block （RFB） and Dense RFB （DRFB） were used to extract deep features. Thirdly， the shallow and deep features were fused， and the fusion result was input into Brightness Adjustment Module （BAM） to adjust the brightness， and finally the enhanced image was obtained. At the same time， a Feature Fusion Module （FFM） was designed in combination with attention mechanism to capture important feature information， which helped to restore dark areas in low-light images. In addition， a joint loss function was introduced to measure the network training loss from multiple aspects. Experimental results show that， compared with RRM （Robust Retinex Model）， Zero-DCE （Zero-reference Deep Curve Estimation） and EnlightenGAN （Enlighten Generative Adversarial Network）， on LOL （LOw-Light） dataset， the Peak Signal-to-Noise Ratio （PSNR） indicator of the proposed network is increased by 49.9%， 40.0% and 18.5% respectively. Meanwhile， the Structural Similarity Index Measure （SSIM） indicator of the proposed network is increased by 20.3%， 50.0% and 34.5% compared with those of the above three on LOL?V2 dataset. The proposed network improves the brightness of low-light images while reducing noise， color distortion and artifacts， resulting in sharper and more natural enhanced images.