To address the problem of low accuracy of point cloud classification and segmentation in the process of multi-target recognition, a point cloud classification and segmentation method DRPT (Double randomness Point Transformer) based on the improved Transformer model is proposed in this paper. The approach creates new point embeddings in the convolutional projection layer of the Transformer model and uses local dynamic processing of local neighborhoods to continuously add global feature attributes in the data feature vector, thus improving the accuracy of point cloud classification and segmentation in multi-target recognition. Standard benchmark datasets (ModelNet40, ShapeNet partial segmentation and SemanticKITTI scene semantic segmentation datasets) are used in the experiments to validate the performance of the model. The experimental results show that the pIoU value of the DRPT model is 85.9%, which is 3.5% higher than other models on average, and effectively improves the accuracy of point cloud classification and segmentation during multi-target recognition detection, which is an effective support for the development of intelligent network technology.