Existing three-dimensional (3D) point cloud semantic recognition and segmentation algorithms often ignore the relationship between the local feature extraction network and the number of network layers, thus failing to resolve the difficulties associated with expanding the network and capturing advanced semantic information when extracting deeper local features. To address these limitations, an algorithm, namely, semantic recognition and segmentation algorithm of 3D point clouds using a multistage hierarchical fusion residual multilayer perceptron (MLP), is proposed. First, the point clouds are sampled in stages with layered structures to ensure that the network can fully extract feature information at various depths. This involves grouping the sampled points to build local neighborhoods, which enhances the ability of the network to mine local features. Each neighboring domain uses the expandable feature extraction operator of the residual MLP block to extract special information of the cloud. Finally, deep semantic information is integrated with shallow geometric information using interpolation and skip connections. The results reveal that the proposed algorithm achieves a recognition accuracy of 95.1% on the ModelNet40 dataset and a segmentation accuracy of 86.6% on the ShapeNet Part dataset. Thus, this algorithm can effectively extract rich point cloud feature information and offer improved capabilities for 3D cloud semantic recognition and segmentation.