To address the issue of insufficient map reliability in simultaneous localization and mapping (SLAM) caused by dynamic object interference in changing environments, a lightweight map construction algorithm was proposed based on dynamic semantic features. Its core innovation lies in deeply integrating semantic perception capabilities with an octree probability update mechanism, achieving a balance between high precision and lightweight performance in dynamic environments. Firstly, a method based on the region proposal network (RPN) was designed to perform dynamic target recognition and mask extraction on RGB-D images. Then, through 3D point cloud back-projection and the intersection over union (IoU) matching of the two-dimensional masks, semantic labels were assigned to the point cloud clusters. On this basis, an innovative approach was adopted to embed semantic information into the multi-resolution modeling process of the octree structure. Through a dynamic update strategy of observation probability guided by semantics, the dynamic target regions were probabilistically suppressed and the static structures were enhanced and maintained, thereby constructing a lightweight dynamic map that combines geometric integrity and semantic understanding capabilities. Experimental results on the TUM dynamic scene dataset show that, compared to state-of-the-art mapping methods like the dense point cloud maps built by DenseSLAM and the semi-dense maps built by ElasticFusion, the proposed method reduces the number of map points and memory usage by at least 89.68% and 84.89%, respectively. After integrating the binary lightweight semantic map into the SLAM system, the mean absolute percentage error(APE-Mean) and the absolute percentage error-root mean square error(APE-RMSE) are improved by over 16.49% and 16.26%, respectively, demonstrating that the proposed algorithm can maintain high adaptability and accuracy in dynamically changing scenes.