To address challenges such as low positioning accuracy, poor robustness, and weak loop closure detection in traditional LiDAR odometry for outdoor wide range scenes, this paper proposes a LiDAR SLAM algorithm based on LiDAR iris loop closure detection. Firstly, the laser point cloud motion distortion is corrected by combining the IMU pre processing data. Secondly, a lidar iris loop closure detection is established, where aerial views of the laser point cloud are encoded by height information and then converted to lidar iris images. Similarity scores among these iris images are calculated to identify optimal loop closure key frames, effectively enhancing loop closure detection accuracy. Finally, the factor graph backend optimization approach is used to co optimize the loop closure factor, lidar odometry factor, and IMU preintegration factor for obtaining globally optimal solution. The test results on the KITTI dataset show that compared with the ALOAM and LeGO LOAM algorithms, the proposed algorithm improves the positional accuracy by 50% and 37% on average. Additionally, it accurately detects loop closures, significantly enhancing localization accuracy and mapping effectiveness.