The novel view synthesis of dynamic scenes is a critical research direction in both computer vision and computer graphics. Specifically, in complex dynamic scenes, effectively separating the static background from the dynamic foreground and generating high-quality synthetic views remains a significant challenge. In this paper, we propose a method called Spatio‑Temporal Decoupled NeRF (ST-DNeRF). By modeling static and dynamic components separately and combining spatio-temporal modeling with dynamic weighting fusion, ST-DNeRF facilitates the generation of novel views and perspectives at any time from monocular videos. A lightweight alignment loss encourages geometric consistency without external masks or optical flow. Experimental results demonstrate that ST-DNeRF outperforms existing methods across multiple dynamic scene datasets, particularly in scenarios involving rigid motion. It achieves significantly higher PSNR and SSIM scores compared to current techniques. However, there is still room for improvement in handling non-rigid motion. The research presented in this paper offers a novel approach to dynamic scene view synthesis and provides a promising direction for future studies.