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Data-driven intelligent fault diagnosis has made considerable strides. However, collecting sufficient fault information in real production data is extremely challenging. Therefore, a novel method of bearing fault diagnosis based on two-dimensional (2D) images and cross-domain few-shot learning is proposed. Initially, the approach uses multiscale morphology to convert the bearing’s one-dimensional (1D) vibration signal into a 2D image, which preserves the whole information. Second, to address the issue of limited bearing fault data, we extend a substantial amount of natural image knowledge to the converted 2D image based on the improved cross-domain few-shot learning method. A distance-based classifier is employed to prevent the problem of overfitting owing to insufficient data to improve the approach’s classification capacity with few samples. The experimental results demonstrate that, with the limited dataset provided, our method outperforms other prevalent methods and has high feasibility and certain engineering applications.