In traditional visual simultaneous localization and mapping (SLAM) algorithms, it is typically assumed that the objects in the environment are static. Nevertheless, dynamic objects are inevitably present in practical applications. The participation of imaging feature points in feature matching and pose estimation introduces errors that influence back-end optimization and subsequently weaken the robustness of the SLAM system, which makes it impossible to obtain globally consistent pose trajectories and maps. This paper proposes a dynamic SLAM algorithm that combines the feature point extraction of the geometric correspondence network v2 (GCNv2) and adaptive separation of dynamic targets via YOLOv8s-seg semantic segmentation. First, the ORB-SLAM3 framework is utilized to extract the feature points based on GCNv2 and generate the corresponding descriptors. Then, the YOLOv8s-seg network is employed to segment the dynamic objects, and the dynamic targets in the scene are detected using the optical flow method. Meanwhile, the optical flow field is used to statistically measure the dynamic degree of the targets, and the threshold for eliminating dynamic feature points is adaptively adjusted based on the dynamic degree to reduce the pose estimation error of the SLAM system. Finally, the static feature points are fused via nonlinear optimization, loop closure detection, and local mapping in the back-end of ORB-SLAM3. Experimental results obtained using the TUM dataset show that, in highly dynamic scenarios, the root mean square error (RMSE) of the absolute trajectory error (ATE) of the proposed algorithm decreases by an average of 77.75%, and the standard deviation (SD) decreases by an average of 69.75%, compared with ORB-SLAM3. The proposed method also achieves significant progress in comparison with Dyna-SLAM and DS-SLAM in that it effectively enhances the positioning accuracy and tracking robustness of ORB-SLAM3 in dynamic scenarios.