3D object point cloud recognition is an important component of environment perception tasks for intelligent robots. Based on dynamic graph features, this paper proposes a Dynamic Graph Stacked Broad Learning System (DG-S-BLS) network for 3D object recognition. DG-S-BLS extracts high-dimensional features from point clouds using a dynamic graph convolutional network, and then uses the Broad Learning System (BLS) model to classify point clouds based on the overall features of samples. The classification accuracy is further improved by using the Stacked BLS model performed upon the residual of the BLS blocks. Experimental results on the LiDARNet outdoor point cloud dataset show that the classification accuracy of DG-S-BLS reaches 99.5%.