Lidar point cloud segmentation technology plays an important role in intelligent vehicle environment recognition. Due to the problems of near dense and far sparse point clouds, uneven distribution, and the presence of noise in LiDAR, inaccurate point cloud segmentation occurs. A self adaptation DBSCAN with Euclidean joint clustering algorithm is proposed to address the above issues. This method first preprocesses the point cloud data, using through filtering, voxel filtering, and cube filtering to extract, sparse, and denoise the point cloud. Then, it combines the adaptive DBSCAN algorithm and an improved variable threshold Euclidean clustering algorithm to cluster and segment the point cloud. Real scene data was collected for testing, and the results showed improvements in evaluation indicators such as C-H coefficient, contour coefficient, D-B coefficient, and contour coefficient. This indicates that the variable threshold joint clustering algorithm significantly improves the accuracy of point cloud segmentation, effectively improves the intra class consistency and inter class differences of clustering results, and provides a more reliable foundation for object detection and recognition.