In order to address the current limitations of pure laser SLAM under large-scale complex environments, particularly their lack of precision and poor robustness, a tightly-coupled SLAM method based on factor graph optimization has been proposed. The method enhances performance through three core designs. Firstly, designing an integrated inertial odometry factor based on the error-state Kalman filter (ESKF) that fuses multi-sensor information. Secondly, adopting a multi-scale depth-aware feature selection algorithm to extract line, plane, and point features, and optimizing inter-frame matching via coarse-to-fine registration to improve pose transformation accuracy. Thirdly, incorporating integrated inertial factors and laser-inertial odometry factors into the factor graph model, and introducing a rotation-invariant Scan Context-based loop closure detection factor to suppress cumulative errors. Comparative experiments on the public KITTI dataset and a self-collected campus dataset show that, compared with the classical LIO-SAM algorithm, the proposed algorithm reduces the mean absolute trajectory error (ATE) by 25.96% and 23.01% respectively, and the root mean square error (RMSE) by 25.52% and 28.38% respectively in complex scenarios. Experiments verify that the proposed algorithm achieves high mapping accuracy and robustness, and provides an effective technical solution for the engineering application of SLAM in outdoor complex scenarios.