In simultaneous localization and mapping (SLAM), the loop closure detection is a critical step to improve localization accuracy. By identifying loop closures and correcting accumulated errors, the accuracy and robustness of localization can be significantly enhanced. However, most existing LiDAR-based loop closure detection methods primarily rely on low-level features such as coordinates and reflectivity to construct descriptors, failing to fully utilize semantic information within the scene. As a result, these methods often face challenges in terms of accuracy and reliability in complex scenarios. To address these limitations, this article proposes a semantic-assisted intensity scan context method to overcome the insufficiencies of existing approaches. First, the proposed method employs the iterative closest point (ICP) algorithm for coarse registration of two-point clouds, reducing the impact of angular and translational errors on loop closure detection. On this basis, semantic features are integrated with the three-dimensional coordinates and reflectivity information of the point clouds to generate a global descriptor that incorporates multi-level features. Finally, loop closures are determined by calculating the similarity of the descriptors, enabling more reliable detection. Experimental results on the publicly available KITTI dataset show that the proposed method achieves a maximum F1 score improvement of 19.71% compared with the Scan Context algorithm, while reducing the average root mean square error (RMSE) by 36% compared with the lego-loam algorithm. Additionally, real-world experiments in a campus environment show that the proposed method improves the maximum F1 score by 19.23% compared with the LIO-SAM algorithm and by 70.62% compared with the lego-loam algorithm. Furthermore, the average RMSE is reduced by 56.68% compared with LIO-SAM and by 20.7% compared with lego-loam. These results show that the suggested method not only greatly improves the accuracy of loop closure detection but also exhibits greater robustness in diverse scenarios. By incorporating semantic information, this method markedly improves the discriminative capability of descriptors in complex environments, providing new perspectives and methodological support for the development of SLAM technologies.