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In recent years, Simultaneous Localization and Mapping (SLAM) has gradually become a focal point in the field of artificial intelligence applications, such as autonomous driving, and AR/VR, demonstrating its irreplaceable role. Traditional SLAM is based on assumptions of static scenes, resulting in low stability and accuracy in dynamic environments. To address this issue, a semantic SLAM system named BiSeNetV2-SLAM, combining ORB-SLAM2, BiSeNetV2, and optical flow fusion, has been proposed. Firstly, the system utilizes the BiSeNetV2 semantic segmentation network to eliminate potential prior dynamic objects. Subsequently, optical flow tracking is employed to detect the motion states of dynamic objects, achieving semantic SLAM in dynamic scenarios. Additionally, the system enhances feature matching accuracy and efficiency by combining the efficient local image descriptor BEBLID with the ORB feature extraction method. In multiple high-dynamic scenes from the TUM public dataset, In multiple high dynamic range scenes from the TUM public dataset, compared to ORB-SLAM2, the RMSE increased by more than 86%, and the S.D. increased by more than 89%. While maintaining similar accuracy compared to DynaSLAM, there was a significant improvement in processing speed.