Aiming at the difficulty of extracting features and classifying 3D point clouds due to their irregularity and disorder, a 3D point cloud classification method that fuses deep learning and ensemble learning is proposed. Firstly, the deep learning model PointCloudTransformer is trained to extract point cloud features and train base classifiers to establish a base classifier set. Subsequently, a base classifier selection model is designed for ensemble learning, and optimization objectives of the model are diversity and average overall accuracy of base classifiers. To reduce ensemble scale, binary multi-objective beluga optimization algorithm based on improved beluga optimization algorithm is proposed to optimize the base classifier selection model and obtain an ensemble pruning scheme set. Finally, the majority voting is used to ensemble the classification results of each base classifier combination on the test set to obtain the optimal base classifier combination, and an ensemble learning model of point cloud classification based on multi-objective optimization ensemble pruning is obtained. Experimental results on the point cloud classification dataset demonstrate that the method in this paper achieves higher ensemble accuracy with a smaller ensemble scale and can accurately classify multi-class 3D point clouds.