Addressing the trade-off between operational efficiency and localization accuracy in visual SLAM, this paper introduces a monocular visual-inertial SLAM algorithm that integrates point and line features. To construct the point-line reprojection error and optimize the observation volume in front-end vision initialization, the motion recovery structure method (SFM) is employed through 3D reconstruction with a sliding window. The marginalization method uses the removed keyframe information as a priori constraint for nonlinear optimization in the back-end. In addition, the loopback detection algorithm is optimized in combination with the bag-of-words model and four-degree-of-freedom global bitmap to improve the accuracy of dynamic object detection, and the performance of the algorithm is tested. The results show that in the case of no closed loop, the absolute root mean square error of the algorithm proposed in this paper is lower than that of VINS-Mono (0.0625), PL-VIO (0.0401), and PL-VINS (0.0554) for the majority of sequences. In the case of closed loops, the absolute root mean square error of the proposed algorithm in this paper is reduced by 0.0395 and 0.0139 on average over most sequences compared to VINS-Mono and PL-VINS. The proposed algorithm in this paper demonstrates higher accuracy and robustness for improved detection and tracking of dynamic objects.