Abstract: Monocular dynamic scene reconstruction methods based on implicit neural radiance field models suffer from high computational cost and low rendering efficiency, while explicit 3D Gaussian splatting reconstruction approaches often introduce motion blur and fail to maintain structural consistency. To address these limitations, a structured 4D Gaussian splatting framework for monocular dynamic scene reconstruction is proposed. Multi-scale point-cloud initialization produces anchor points that guide local Gaussian modeling, and Gaussian attributes—density, color, and covariance—are predicted in real time from anchor features, viewing direction, and viewing distance; an adaptive anchor growth and pruning strategy continuously refines the Gaussian set, ensuring efficient rendering without quality loss; finally, an annealing-based smoothing mechanism enhances temporal coherence and detail fidelity without increasing model complexity. Experiments on the D-NeRF synthetic dataset and the NeRF-DS real dataset demonstrate that, compared to Deformer-GS, the proposed method achieves PSNR improvements of 2.7% and 3.3%, and SSIM gains of 0.5% and 4.1%, respectively, demonstrating superior reconstruction accuracy, structural consistency, and cross-view robustness.