Aiming at the problems of missing identification of pole columns and misclassification of pole tops caused by partial missing of vehicle-mounted laser point cloud, a progressive pole target identification method considering spatial features is proposed. The method effectively solves the problem of accurate identification and classification of intermittent pole targets in the point cloud by using a cascaded random forest model and spatial relationship features. Firstly, the spatial relation features between continuous arcs in the vertical direction are obtained by the arc morphology features of the top model of the cascade structure in the multi-scale node slices, and the accurate identification of intermittent poles is achieved by combining the arc morphology features; secondly, based on the pole identification results of the cascade model, the top clusters of the vertical pole clusters are obtained, and the spatial features of poles and pole tops are obtained by the spatially relationally constrained ESF shape features combined with PCA dimensional features using the cascade model to achieve accurate classification of pole tops. The experimental results show that the recognition accuracy of this method reaches 96.56% and 94.51% in two sets of experimental data, which can effectively deal with the recognition and classification of pole targets in complex road scenes and has strong stability.