Abstract: Deep learning is gaining attention in the field of mechanical equipment fault diagnosis. With the help of deep learning techniques, deep neural networks (DNNs) have great potential for machinery fault diagnosis. Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers to deliver state-of-the-art accuracy in various classifications of mechanical rotating parts. Convolutional neural networks (CNNs) are able to automatically learn multiple levels of representations from raw input datawithout introducing hand-coded rules or domain knowledge. Because of this powerful representation learning ability, deep learning has achieved great success in many fields. Although deep learning has achieved promising results in the field of machinery fault diagnosis, existing neural networks suffer from many limitations. The heavy and complex calculation amount puts forward strict requirements for computer hardware, which severely limits its application in actual engineering. To address this issue, this paper proposed a novel lightweight neural network model, ShuffleNet, for high-speed train wheelset bearing fault diagnosis. Based on the thought of module design, this model comprised several ShuffleNet units. Group convolution (GC) and deep separable convolution were used to improve the operation efficiency of traditional convolution in the ShuffleNet unit. Meanwhile, channel shuffle (CS) technology was adopted to overcome the grouping constraint caused by GC and improved the loss accuracy ofthenetwork model. CS operation makes it possible to build more powerful structures with multiple GC layers. Experimental results show that the proposed network model canbe applied in wheelset bearing fault diagnosis underacomplex working condition. Compared to the traditional CNN, ResNets, and Xception, the proposed method can greatly reducethecomputation cost while maintaining diagnosis accuracy. It is clear that the proposed lightweight neural network model, ShuffleNet, is superior to the above comparison models. This provides a new way forengineering applications of DNN technology and overcoming the limitations of computer hardware.