DDN-SLAM is a novel real-time dense dynamic neural implicit SLAM system that integrates semantic features to address tracking drift and mapping errors in dynamic environments. The system introduces a feature point segmentation method that combines semantic features with a Gaussian mixture distribution model to handle dynamic tracking interferences. It also proposes a mapping strategy based on sparse point cloud sampling and background restoration to avoid incorrect background removal. A dynamic semantic loss is used to eliminate dynamic occlusions. Experimental results demonstrate that DDN-SLAM can robustly track and produce high-quality reconstructions in dynamic environments while preserving potential dynamic objects. Compared to existing neural implicit SLAM systems, DDN-SLAM achieves an average 90% improvement in Average Trajectory Error (ATE) accuracy on dynamic datasets. The system is evaluated on various datasets, including TUM RGB-D, Bonn, OpenLORIS-Scene, Replica, ScanNet, and EuRoC, showing superior performance in dynamic and challenging scenarios.DDN-SLAM is a novel real-time dense dynamic neural implicit SLAM system that integrates semantic features to address tracking drift and mapping errors in dynamic environments. The system introduces a feature point segmentation method that combines semantic features with a Gaussian mixture distribution model to handle dynamic tracking interferences. It also proposes a mapping strategy based on sparse point cloud sampling and background restoration to avoid incorrect background removal. A dynamic semantic loss is used to eliminate dynamic occlusions. Experimental results demonstrate that DDN-SLAM can robustly track and produce high-quality reconstructions in dynamic environments while preserving potential dynamic objects. Compared to existing neural implicit SLAM systems, DDN-SLAM achieves an average 90% improvement in Average Trajectory Error (ATE) accuracy on dynamic datasets. The system is evaluated on various datasets, including TUM RGB-D, Bonn, OpenLORIS-Scene, Replica, ScanNet, and EuRoC, showing superior performance in dynamic and challenging scenarios.