This paper proposes a multi-source fusion SLAM method based on LiDAR/IMU/UWB tight coupling to address the problem of decreased positioning accuracy caused by dynamic interference in urban environments. The aim is to improve the robustness and localization accuracy of the system under dynamic interference. In the odometery stage, this paper constructs a factor graph framework that deeply integrates IMU pre integration and multi base station UWB distance observation. IMU provides high-frequency motion priors to compensate for point cloud distortion, while UWB introduces stable external constraints through absolute ranging without cumulative errors, which can effectively suppress the cumulative errors of LiDAR in dynamic environments and significantly improve the robustness of pose estimation in dynamic interference scenarios. In addition, in the loop detection and global optimization stage, this paper designs an extended descriptor that combines UWB information to encode and fuse point cloud geometric features with UWB absolute range and signal strength information, forming a more discriminative scene representation. Based on this descriptor, a two-level loop detection strategy of "coarse retrieval fine verification″ is adopted: First, UWB information is used to quickly screen candidate loop frames, and then point cloud descriptor geometry verification is performed, effectively improving the accuracy of loop detection in dynamic and homogeneous environments. After detecting the loop, the UWB historical information and point cloud constraints are jointly incorporated into the global factor map for collaborative optimization, further improving trajectory consistency and map closure accuracy. Experiments real-word urban dynamic-scene datastes show that our method can significantly reduce absolute trajectory error (ATE) compared with methods such as FAST-LIO2 and MR-ULINS. Meanwhile, the UWB assisted loop back detection mechanism performs better on the precision recall curve, effectively reducing dynamic ghosting and false matching, verifying the effectiveness and superiority of this method in urban dynamic environments.