LiDAR point cloud data serves as a crucial data source for forest resource inventory. However， the registration of multi-view terrestrial LiDAR point cloud data in forest scenarios is often plagued by inefficiency. Addressing the limitations of current research， this study proposes a target-free and rapid point cloud registration method based on point cloud normal features as the feature descriptor. Firstly， the original point cloud is subjected to denoising and voxelization processing. Then， point cloud normal vectors are computed， and feature matching is performed. Finally， precise registration is achieved using the nearest neighbor iterative algorithm. The proposed method is tested in field plots with different vegetation characteristics thus registration difficulty levels. Experimental results show that the optimal voxelization sampling interval is between 30 cm and 50 cm. Compared to the reference results derived from manual registration， the average horizontal translation error and average vertical translation error are 3.13 cm and 0.86 cm， respectively， while the average rotation error is 1.39 '. The average processing time is 5.2 s， and the average point-wise error is 6.5 cm. This method successfully improve the efficiency and accuracy of automated point cloud registration in complex forested environment.