目的 通过三维扫描仪得到的点云数据往往存在很多异常值，例如噪点、遗失点和外部点等。在这些异常值存在的情况下，为了提高三维点云数据的分类精度，提出一种基于集成学习的强鲁棒性三维点云数据分类方法。方法 提出一种基于最大投票法的集成学习思想，将2个深度神经网络的分类结果进行集成，从而提高网络的泛化性和准确性;采用全局特征增强和中心损失函数来优化神经网络结构，提高分类精度并增强鲁棒性。结果 文中方法缩短模型训练时间至30个迭代次数，且在有噪点、丢失点和外部点的情况下分类精度均得到有效提升。结论 提出的EL-3D算法在含有噪点、丢失点和外部点的情况下，鲁棒性效果要优于目前的点云分类方法。

There are many outliers in the point cloud data obtained by 3D scanner, such as noise, missing points and external points. The work aims to propose a strong robust 3D point cloud data classification method based on ensemble learning to improve the classification accuracy of 3D point cloud data in the presence of these outliers. An idea of ensemble learning based on maximum voting method was proposed, which integrated the classification results of two deep neural networks to improve the generalization and accuracy of the network; global feature enhancement and central loss function were used to optimize the neural network structure to improve the classification accuracy and robustness. The proposed method shortened the training time to 30 epochs, and improved the classification accuracy effectively in the case of noise, missing points and external points. The proposed el-3d algorithm has better robustness than the current point cloud classification methods in the case of noise, missing points and external points.