Aiming at the classification problems of Three-Dimensional （3D） models， a method of few-shot recognition of 3D models based on Transformer was proposed. Firstly， the 3D point cloud models of the support and query samples were fed into the feature extraction module to obtain feature vectors. Then， the attention features of the support samples were calculated in the Transformer module. Finally， the cosine similarity network was used to calculate the relation scores between the query samples and the support samples. On ModelNet 40 dataset， compared with the Dual-Long Short-Term Memory （Dual-LSTM） method， the proposed method has the recognition accuracy of 5-way 1-shot and 5-way 5-shot increased by 34.54 and 21.00 percentage points， respectively. At the same time， the proposed method also obtains high accuracy on ShapeNet Core dataset. Experimental results show that the proposed method can recognize new categories of 3D models more accurately.