To address the limitations of most visual simultaneous localization and mapping (SLAM） systems in terms of positioning accuracy and robustness within indoor dynamic environments, this paper proposes a real-time semantic SLAM visualization system based on an enhanced Photo-SLAM algorithm framework. First, a lightweight semantic segmentation network is added to the original algorithm framework to remove dynamic object features from the image. Additionally, an adaptive edge expansion method is introduced to eliminate residual dynamic feature points caused by insufficient edge detection in semantic segmentation, further enhancing the system′s localization accuracy in dynamic environments. Second, semantic information is introduced into the 3D Gaussian Splatting process to remove the Gaussian bodies of dynamic objects, constructing a static, smooth, and continuous 3D Gaussian map. Finally, a dense point cloud mapping thread is added to the Photo-SLAM algorithm framework to construct a dense point cloud map of the static background based on semantic information and key frames, and multiple filtering methods are added to further remove the influence of outliers on the point cloud map. Experimental results on the TUM dataset show that the improved algorithm achieves over 40% higher localization accuracy than the Photo-SLAM algorithm in low-dynamic scenes and over 93% higher accuracy in high-dynamic scenes. Compared to other dynamic SLAM algorithms, the proposed algorithm achieves higher localization accuracy in most scenarios and is more real-time. It can also create 3D Gaussian maps and dense point cloud maps after removing the interference of dynamic objects, enabling visualization of the static background.