Distributed acoustic sensing (DAS) is a novel and revolutionary technology that is widely used in the field of seismic exploration. However, the problem of low signal-to-noise ratio (SNR) has always been a serious challenge affecting its processing and interpretation. At present, deep learning-based algorithms show remarkable potential in DAS vertical seismic profile (VSP) data denoising, but the recovery of deep-layer weak signals still needs to be further improved. To solve the above problems, we propose a dual attention denoising network (DADN) combining spatial attention (SA) and channel attention (CA) double attention block (DAB) to improve the recovery effect of deep-layer weak signals. The DADN consists of multiple DABs and utilizes encoder–decoder structure to extract signal features at multiple scales. At each scale, DADN uses a DAB to reassign the weights of feature maps on each channel and region to focus on useful information, which is beneficial for accurately extracting signal features and recovering deep-layer weak signals. The information flow undergoes downsampling and upsampling operations to finally achieve an accurate estimation of the DAS VSP signals. In addition, gradient-weighted class activation mapping (Grad-CAM) is introduced to visually interpret the signal features learned by the network. The visualization results show that the network does accurately distinguish between signals and noise. After training on the constructed semisynthetic DAS VSP dataset, DADN demonstrated competitive performance in both noise suppression and weak signals retention.