Abstract: The disorder, sparseness and finiteness of point cloud data make it difficult to classify point cloud models based on deep learning. The existing point cloud-oriented deep learning networks have the problems of complex model structures and many training parameters, which make it difficult to apply for real-time point cloud recognition tasks. To address these problems, a lightweight real-time point cloud network, LightPointNet, is proposed. Firstly, based on the characteristics of point cloud models and the design principle of lightweight point cloud classification network, a prototype of deep learning network for point cloud model classification is proposed. Then, the network parameters are optimized and the final point cloud network LightPointNet is formed using variable-controlling approach. The network is compact in structure, consisting of only 3 layers of convolution, 1 layer of pooling and 1 layer of full connection, and the number of parameters is less than 0.07 M. Experiments on ModelNet40 dataset have shown that LightPointNet improve the classification accuracy rates of PointNet, VoxNet, and LightNet by 0.29%, 6.49%, and 2.59%, and its parameter size is reduced by 98.00%, 92.40%,and 76.60%, respectively. Experiments on MINST and SHREC15 have shown that LightPointNet has universal adaptability for wide variety of point cloud data. This result demonstrates that the LightPointNet achieves high classification performance, high computational efficiency, lightweight and real-time advantages. Therefore, the network can be deployed in embedded devices and has a broad application prospect in the Internet of Things,point cloud real-time processing and so on.