In the field of unmanned vehicles, point cloud strength and ground constraints play a very important role in mapping and positioning under large-scale environment. However, existing laser SLAM algorithms only consider geometric features when constructing maps, and neglect point cloud intensity information and ground constraints, resulting in blurry mapping details and drifting in the Z-axis direction, thereby reducing the accuracy of SLAM systems. To this end, this paper proposes a laser SLAM optimization algorithm based on point cloud intensity and ground constraints. Based on the ground measurement model, it is proposed to construct local conditional ground constraints, which not only improves the accuracy of ground point extraction but also reduces the drifting in the Z-axis direction; introducing point cloud intensity information to improve the reliability of non-ground point clustering, further improving mapping accuracy and positioning stability. A feature extraction method based on local smoothness is proposed, in which by introducing intensity factors to rank intensity features, features with consistent intensity information are selected preferentially, enhancing the robustness of feature extraction. The pose is optimized and estimated by constructing strength residuals based on a spherical strength map, together with geometric residuals, effectively solving the problem of blurring in map details in odometry. The matching distance and intensity difference based on feature projection are used to remove interference from dynamic point clouds, further improving the robustness of SLAM systems. Experiments on the public dataset KITTI and real scenarios have shown that the proposed algorithm has higher mapping and positioning accuracies by introducing ground constraints and point cloud strength information. Compared to the LVI-SAM algorithm that outperforms traditional LIO-SAM algorithm, the proposed algorithm in this paper is improved by 54.5% in accuracy, providing a reliable solution for SLAM tasks of unmanned vehicles in large-scale environment.