The visual simultaneous localization and mapping algorithm is easy to be interfered under occlusion, which leads to large positioning error and low closed-loop detection accuracy. In this paper, a visual simultaneous localization and mapping algorithm based on mixed attention instance segmentation is proposed, which can dynamically adjust the recognition weight of the occluded object and improve the feature extraction and recognition ability of the occluded object in the case of occlusion. At the same time, a probabilistic mismatching removal algorithm is used to remove the wrong matching point pairs and increase the accuracy of pose solution and key frame selection. In this way, the robot pose can be better corrected and the accuracy of system composition can be improved. The proposed algorithm is tested through KITTI open dataset and real scenes, and the results show that the closed-loop accuracy of the proposed algorithm is about 10.7% higher than ORB-SLAM2 algorithm, and the translation error is about 27.6% lower, reflecting good composition ability.