Abstract: Unmanned surface vessels (USVs) have autonomous navigation capacities under complex water surface environment, which is the basis for aquaculture operation, with great development prospects in intelligent aquaculture. Simultaneous localization and mapping (SLAM) technology provides real-time environmental information, playing a pivotal role in enabling autonomous navigation for USVs. However, water surfaces present low-texture scenes with insufficient distinct features, compounded by dynamic invalid features caused by surface ripples and reflections, which result in poor calculation accuracy and serious performance degradation of visual SLAM pose. To address this problem, the paper proposes a SLAM system oriented towards dynamic water surface environments based on visual semantics and point-line fusion. Firstly, the ORB-SLAM3 framework is enhanced by introducing a semantic segmentation thread to eliminate invalid water features using semantic masks, thereby mitigating dynamic water interference. Secondly, the system stability is enhanced by incorporating line features to propose a geometric-constraint-based line-matching method to improve the accuracy of waterline feature extraction and tracking. Moreover, the point-line feature fusion is utilized to enhance data association accuracy, resolving the deficiency of traditional SLAM algorithms in extracting features from low-texture water surfaces. The results of the USVInland dataset demonstrate that compared with ORB-SLAM3 and PL-SLAM algorithms, the positioning accuracy of the improved algorithm has averagely increased by average of 44.74% and 55.48% in straight-line navigation, and up to 76.60% and 70.15% in maneuvering navigation, which mitigates the impact of water surface disturbances on pose estimation effectively, and enhances the accuracy and robustness of visual SLAM systems under low-texture water environment.