Low Light Image Enhancement, which is to restore the image acquired under the condition of insufficient light to the normal exposure image. Most of the existing low-light image enhancement algorithms obtain good enhancement effect by designing complex network structure, and the computational efficiency is low. The enhanced image will still have problems such as increased noise, color distortion and detail loss, which will affect visual perception and subsequent advanced visual tasks. Therefore, a lightweight low-light image enhancement method based on multi-attention feature fusion is proposed in this paper. Simple gate attention module is used to extract the global features of low-light images effectively, and the computational overhead is reduced and image details are preserved by simplifying the channel attention and gating unit. The multi-attention fusion module is used to integrate the information of global features and local features extracted from local receiving fields, and enhance the representation of channel attention and spatial attention to global and local features through pixel attention, so as to better restore image color and suppress noise. In addition, the joint loss function is used to constrain the enhancement task, and extensive experiments on real data sets show that the performance of the proposed method exceeds the current advanced low-light image enhancement methods, and has good computational efficiency and generalization ability.