The oscillating scanning pattern of Livox lidar leads to challenges such as drag deformation and sparse point clouds when scanning and sampling moving objects in real-time. This paper introduces an object detection and classification algorithm designed to mitigate motion distortion and enhance point cloud density. Firstly, the algorithm uses background extraction to separate the background points cloud, and then uses the priori rotation translation matrix to fuse the 5 frames point cloud data. At the same time, it uses the Fast Euclidean Clustering algorithm with dynamic threshold to cluster point clouds. Then the geometry information, intensity, histogram of oriented gradient and other features of the object point clouds are extracted to train the Support Vector Machine classifier to achieve the object classification. Finally, the classification performance of the algorithm is analyzed through evaluation indicators such as the precision and recall. The experimental results show that the algorithm has excellent classification and real-time performance for different objects under the condition that the lidar is deployed in the urban intersection environment.