Traditional approaches integrating 3D Gaussian splatting (3DGS) into simultaneous localization and mapping (SLAM) are often limited to static scenes or scenarios with known camera poses. To enable fast and accurate localization and modeling in dynamic environments, this study proposes a tracking and reconstruction method based on motion representation for dynamic scenes. The tracking process computes camera poses via generalized-iterative closest point (G-ICP) and uses an optical flow-guided motion segmentation model to identify dynamic object masks for separating moving objects from static backgrounds. The mapping process leverages red green blue-depth (RGB-D) data and long-range 2D trajectories as data priors, modeling moving objects using the 3D Gaussian splatting framework based on special Euclidean motion bases. Experimental results demonstrate that the optical flow-guided motion segmentation model improves extraction precision by 23.2% compared to the original SAM 2 model. For static scene reconstruction, the proposed method achieves real-time modeling at 98 frame/s on the Replica dataset and 75 frame/s on the TUM RGB-D dataset, and high-precision scene reconstruction is realized on both datasets. In dynamic scenarios, the proposed algorithm reduces absolute trajectory error by 78.30% compared to ORB-SLAM3 algorithm, 61.35% compared to RDS-SLAM algorithm, and 36.05% compared to DS-SLAM algorithm, verifying its superior localization precision and robustness in complex dynamic environments.