With the popularity of 3-D sensors in industrial Internet of Things (IIoT), point clouds learning is increasingly important. In this article, we propose a novel multilevel attention based U-shape graph neural network (MAUGNN) for point clouds learning, which can effectively learn the features from low-level to high-level and fuse multiple-level features based on the graph neural networks and attention mechanism. There are three parts in MAUGNN: encoder, decoder, and connections. In the encoder and decoder, we design an attention-based graph convolution to explore the structural information for point clouds. During the encoder, a structure-aware attention pooling is proposed to support down-sampling on point cloud data. To adaptively fuse coarse-grained features from the encoder and fine-grained features from the decoder together, we also propose a structure-aware attention skip connection mechanism. Extensive experiments on popular point cloud datasets demonstrate the superior performance of our MAUGNN over state-of-the-art baselines.