We present MCGS (Markov Chain Gaussian Splatting), a novel approach for high-fidelity dynamic scene reconstruction via combining Markov chain and 3D Gaussian splatting. Our method addresses the critical challenge of artifact-free temporal consistency in dynamic neural rendering. By integrating a Markov chain-based deformation network with multi-head temporal attention, MCGS effectively captures motion patterns and temporal dependencies, producing more accurate and stable 3D representations over time. The key innovations include: (1) a Markov Deform Network that models state transitions while preserving temporal coherence, (2) a temporal attention mechanism that adaptively weights historical states within a sliding window, and (3) strategic noise injection during training to enhance model robustness and generalization. Experiments on representative dynamic scene datasets demonstrate that MCGS outperforms previous methods in both visual quality and temporal coherence, while maintaining competitive rendering speed and efficiency. These results suggest the practical applicability of our approach to real-world dynamic scene understanding and synthesis.