Abstract: The current painting market is full of forgeries, which brings great economic risks to the collectors and disrupts the order of the painting market. Since the data set for the authenticity identification of calligraphy and painting artworks are generally small, this paper proposed a data-efficient algorithm for automatic authenticity identification of Qi Baishi shrimp paintings. The proposed method takes the visual transformer as the backbone and improves the token embedding strategy of the visual transformer with relative position embedding. Besides, this paper trained the model with cross-architecture representation knowledge distillation, which improves the inductive bias of the visual transformer, and reduces the demand of training data for the model. The proposed method effectively improves its performance in a small dataset of Qi Baishi shrimp painting authenticity identification. Experiments performed on the authenticity identification dataset of Qi Baishi shrimp paintings with 429 paintings, the WikiArt dataset with 96 013 paintings, and the ArtDL dataset with 42 479 paintings show that the proposed method can effectively identify the authenticity of Qi Baishi shrimp paintings.