Traditional semantic Simultaneous Localization and Mapping （SLAM） systems rely on training with full data， making it difficult to recognize novel categories in open environments. In addition， the systems only construct sparse maps， resulting in limited semantic representation capabilities and insufficient support for complex human-machine interaction. Therefore， an incremental semantic mapping system based on visual SLAM was proposed. In the system， a dense mapping thread was introduced into the ORB-SLAM3 （Oriented FAST and Rotated BRIEF SLAM 3.0） framework， and a newly designed semantic segmentation model， DAISS （Depth Attention Incremental Semantic Segmentation）， was integrated， so as to achieve simultaneous localization and semantic mapping. Among which， a dual-branch encoder and an attention distillation mechanism were adopted by the DAISS model to improve segmentation accuracy for old and new categories. Experimental results on NYU Depth V2 dataset show that this system achieves 61.0% and 47.5% in Mean Accuracy （MA） and Mean Intersection over Union （MIoU） metrics， representing improvements of 60.9% and 48.9%， respectively， over the SATS （Self-Attention Transfer for continual Semantic segmentation） model. These results validate its strong incremental learning capability and semantic perception performance.