Images in real-world scenarios are affected easily by external lighting conditions or camera parameters， resulting in issues such as low overall brightness， poor visual effects， and much noise. These problems lead to difficulties in subsequent face recognition tasks， thereby causing engineering challenges. Therefore， an adaptive low-light face recognition network based on feature fusion， named LLANet （Low Light Adaptive face recognition Network）， was proposed with four parts： a decomposition subnet， a restoration subnet， an adjustment subnet， and a backbone network. Low-light and normal-light images were used as inputs. Firstly， based on Retinex theory， the input low-light and normal-light images were decomposed into the corresponding illumination and reflection maps. The illumination map was input into the adjustment subnet， where an attention mechanism was introduced to focus on lighting features， thereby enhancing the performance of low-light image enhancement and ensuring quality of the enhanced images. At the same time， the reflection map was input into the restoration subnet for detail restoration and noise reduction， thereby addressing degradation and noise issues of the reflection map in low-light images. And features of output of the adjustment and restoration subnets were fused to obtain the enhanced feature map. Then， to accomplish downstream face recognition tasks as well as prevent overfitting of lighting features and inaccuracies in face feature extraction， a weighted feature fusion strategy was adopted to combine the original face features extracted by the backbone network with the enhanced feature map， resulting in a feature map with richer information. Finally， an Adversarial Data Augmentation （ADA） strategy was introduced to generate more hard samples during training， thereby addressing the ill-posed problem while reducing the influence of alignment errors caused by low-light images during face detection phase， as a result， the network performance was further improved. Experimental results on CASIA-FaceV5， SoF， and YaleB low-light face datasets demonstrate that LLANet has the recognition rates reached 94.67%， 98.22%， and 97.24%， respectively， which are improved by 2.14， 1.58， and 2.10 percentage points on the three datasets， respectively， compared with ARoFace （Alignment Robust Face）. It can be seen that LLANet achieves high recognition accuracy in low-light scenarios.