In recent years, traditional geophones for well seismic data acquisition have progressively been replaced by distributed acoustic sensing (DAS), a novel technique. The primary attributes of DAS are its extensive well coverage and robust adaptability to challenging acquisition situations. Unlike conventional geophones, vertical seismic profile (VSP) data obtained using DAS exhibit lower signal-to-noise ratios (SNRs) and more complex noise types. These complex and energetic perturbations pose challenges for further data analysis. Contemporary methods for mitigating noise in DAS-VSP data sometimes fail to yield complete and precise information, leading to inferior denoising quality and diminished signal recovery. We propose a hierarchical division encoder–decoder network utilizing a convolutional neural network to address this issue. This network employs spatial attention techniques for systematic reconstruction and facilitates hierarchical feature extraction according to information scale. Our methodology provides superior noise reduction capabilities while preserving signal integrity. It achieves this by comprehensively addressing features at all scales. Additionally, we generated the required training set by combining synthetic data with real noise, as no publicly available training sets are available for DAS-VSP data. The trained denoising network processes and analyzes both synthetic and real recordings. The experimental results demonstrate the efficacy of this technique in eliminating various types of DAS-VSP noise while preserving signal amplitude integrity and ensuring continuity of signal recovery.