Deep learning-based reconstruction methods for Video Snapshot Compressive Imaging （VSCI） have achieved promising results in many tasks. However， challenges such as insufficient detail recovery and high computational overhead remain in dynamic scene reconstruction. To address these issues， a VSCI reconstruction method based on dense spatio-temporal deformable attention was proposed. First， the compressed measurements and masks were input to obtain initial feature representations. Second， a deformable attention module was designed to enhance the model's ability to capture local deformations and global temporal dependencies effectively. Finally， the dense connectivity was improved by designing a channel splitting factor to enable dynamic group-wise progressive feature extraction and fusion， thereby improving feature representation ability and reducing reconstruction time. Experimental results on multiple simulated grayscale video benchmark datasets （e.g.， Kobe， Runner， Drop） and color video benchmark datasets （e.g.， Beauty， Bosphorus， Jockey） showed that compared with the suboptimal methods M2BA-SCI and SCT-SCI， the proposed method improves the average Peak Signal-to-Noise Ratio （PSNR） by 0.45 dB and 0.79 dB， with reconstruction times of 0.43 s and 3.37 s respectively， demonstrating that it significantly enhances the reconstruction quality and computational efficiency for complex motion scenes.