To address the issues of insufficient long-range motion modeling and over-parameterization caused by channel redundancy in video steganography tasks under small-sample conditions， an enhanced video steganographic network — GAB3D-SEVSN was proposed by integrating 3D Global Attention Block （GAB-3D） and Squeeze-and-Excitation （SE） channel attention. In the model， through the optimized GAB-3D module， key motion trajectories in the 3D spatio-temporal domain were focused on adaptively， thereby enhancing the capability of modeling long-range dependencies. Meanwhile， by embedding the SE module into the reversible architecture， the channel-level adaptive calibration was achieved， which suppressed redundant parameters and alleviated over-parameterization effectively. Experimental results on the UCF101 dataset （13K video samples） demonstrate that， compared to the LF-VSN baseline model， the proposed model achieves improvements of 0.5 dB in Peak Signal-to-Noise Ratio （PSNR） and 2.06% in Structural SIMilarity （SSIM）. Ablation experimental results verify the effectiveness and synergistic effect of various modules. Test results on high-dynamic scene subsets and videos with different attributes show that the model outperforms baseline models in PSNR and SSIM significantly， demonstrating excellent robustness and generalization ability.