Laser point cloud matching is a key factor affecting the accuracy and efficiency of laser SLAM systems. Traditional laser SLAM algorithms cannot effectively distinguish scene structures and result in performance degradation due to poor feature extraction in unstructured scenes. To address this issue, a joint coherent point drift (CPD) adaptive laser SLAM algorithm for complex scenes is proposed, called CPD-LOAM. First, a scene structure identification method combining prejudgment and verification is proposed, in which scene feature variables are introduced to make preliminary judgments on the scene structure. Then, surface curvature is further used to verify the preliminary judgments from the perspective of geometric features, enhancing the accuracy of scene structure identification. In addition, the CPD algorithm is combined for point cloud pre-registration in unstructured scenes, and then the ICP algorithm is used for re-registration to solve the problem of feature degradation in this scene, thereby improving the accuracy and efficiency of point cloud registration. The experimental results show that the proposed scene feature variables and surface curvature can effectively distinguish structure scenes based on the set threshold. The validation results on the public dataset KITTI show that CPD-LOAM reduces the positioning error by 84.47% compared to the LOAM algorithm, and improves the positioning accuracy by 55.88% and 30.52% respectively, compared to the LEGO-LOAM and LIO-SAM algorithms, with higher efficiency and robustness.