To address the low accuracy of the laser simultaneous localization and mapping (SLAM) algorithm in large scenes, this paper proposes a lidar SLAM algorithm based on point cloud geometry and intensity information. First, point cloud geometry and intensity information are combined for feature extraction, where weighted intensity is introduced in the calculation of local smoothness to enhance the robustness of feature extraction. Subsequently, a rectangular intensity map is introduced to construct intensity residuals, whereas geometric and strength residuals are integrated to optimize pose estimation, thereby improving mapping accuracy. Finally, a feature descriptor based on point cloud geometry and intensity information is introduced in the loop detection process, thus effectively enhancing the loop recognition accuracy. Experimental results on public datasets show that, compared with the current mainstream LeGO-LOAM algorithm, the proposed algorithm offers greater position accuracy.