High-precision mapping and positioning is an important prerequisite for autonomous navigation of unmanned vehicles, and in view of the insufficient utilization of observation information by existing loosely coupled fusion methods and the low matching accuracy of traditional closed-loop detection methods, a tightly coupled laser SLAM method based on scan context optimization, named optimized SC-LIO-SAM, is proposed. First, the IMU debiases the point cloud through pre-integration and provides initial pose estimation for the laser odometer. The laser odometer matches the feature point cloud of the current frame with the local map by sliding the window. Then, based on the scan context, the feature cloud is encoded to generate point cloud descriptors to achieve efficient closed-loop detection. Based on the LIO-SAM framework, the IMU pre-integration factor, laser odometry factor, GPS factor and closed-loop factor are inserted into the global factor gragh, lastly the global node optimization is updated by using Bayesian tree. In order to verify the effctiveness of the proposed method, the performance of the optimized SC-LIO-SAM is evaluated by KITTI dataset and compared with LOAM, LEGO-LOAM and LIO-SAM, and the experimental results show that the positioning accuracy of the optimized SC-LIO-SAM is significantly improved compared with LOAM, LEGO-LOAM, LIO-SAM and the classical algorithm. Finally, the algorithm is applied to the open-source dataset to prove that the optimized SC-LIO-SAM can build a globally consistent map.