Recently， with the advancement of multi-source capturing devices and sensing technologies， screen recapturing has become a primary channel for video information leakage during dissemination. The screen-recapturing process causes irreversible physical damage to video watermark information， making it difficult to trace the source of information leakage， which poses a significant challenge in the field of video forensics. The existing anti-screen-recapturing video watermarking techniques are still in the preliminary research stage， and are mainly relying on handcrafted frequency-domain features， which struggle to resist cross-device screen-recapturing attacks effectively. Therefore， an end-to-end anti-screen-recapturing video watermarking method was proposed on the basis of a spatio-temporal feature enhancement network. First， to enrich watermark embedding features， a spatio-temporal feature enhancement network module was designed by integrating an optical flow estimation network and a high-pass filter， thereby optimizing the watermark information embedding strategy. Then， by introducing key factors of screen-recapturing channel noise to simulate screen-recapturing distortion， an anti-screen-recapturing attack noise layer was designed to enhance cross-device robustness against such attacks. Experimental results in real-world screen-recapturing attack scenarios demonstrate that the proposed method achieves a decoding accuracy of over 95%， with both robustness and image quality outperforming the mainstream solutions such as Robust Inherent Video Information Embedding （RIVIE） and Automatic， Robust and Blind Video waterMarking resisting camera recording （ARB-VM）.