Abstract: Existing few-shot Chinese font generation models struggle to effectively decouple font style and content using a limited number of reference characters, leading to suboptimal font generation results. To address this issue, a novel few-shot Chinese font generation method based on contrastive learning and square-block transformation data augmentation is proposed. Utilizing the concept of contrastive learning, the output images undergo separat e style and content comparisons, achieving better adaptive decoupling of font style and content. To further improve the performance of few-shot font generation, a data augmentation method based on square-block transformations is introduced, embedding the global structural information of the fonts through geometric transformations of the square-block areas, thereby enhancing the ability to decouple font style and content. The effectiveness of the proposed method is validated through extensive experiments on public Chinese font datasets. Experimental results demonstrate that, compared to current state-of-the-art few-shot font generation models, the proposed method significantly improves the quality of generated fonts and outperforms comparative models in performance metrics such as FID, SSIM, PSNR, and LPIPS. Notably, the proposed method can be integrated into existing few-shot font generation models and, with only a small training dataset, can significantly enhance the performance of these models.