Adversarial attacks pose a significant threat to deep neural networks (DNNs) used for 3-D point cloud classification, especially in safety-critical applications. While previous works have proposed several defense model architectures and adversarial training strategies, they often either fall short in capturing the intricate geometric and topological aspects of point cloud data or grapple with challenges pertaining to model convergence. To solve these problems, in this article, we propose an innovative point cloud defense framework, called MAFFN-SAT, which contains a multiview adaptive feature fusion network (MAFFN) along with a smooth adversarial training (SAT) strategy. Specifically, we construct a multiview defense module to obtain multiview features in MAFFN, which uses geometric proximity and spatial queries to comprehensively explore the inherent characteristics of point cloud data. Subsequently, an adaptive feature fusion module is designed to integrate the multiview features. Furthermore, we introduce SAT, which uses an optimized regularization to measure the information divergence between two probability distributions, guiding the model to develop a smoother decision boundary, thereby more robust to adversarial attacks. Extensive experiments conducted on three benchmark datasets demonstrate the robustness of our approach against various attacks. Remarkably, our defense framework achieves 15.34% performance improvement under point dropping attacks on the ModelNet40 dataset. Our implementation: https://github.com/shenyu234/MAFFN-SAT .