Rolling bearings, as a type of precision mechanical component, are widely used in modern industrial machinery and equipment. It is of great significance to diagnose bearing faults using reasonable methods during bearing operation. However, in the actual complex and ever-changing environment, the collection of vibration signals often faces challenges such as limited sample sizes, noise interference, and operating condition variations, resulting in low fault diagnosis accuracy. To address the problem of small-sample rolling bearing fault diagnosis under noise interference and variable operating conditions, this paper proposed a meta-learning denoising model based on prototype domain enhancement (Meta-DAE). Firstly, a small-sample fault dataset based on time-frequency diagrams was constructed, and a deep convolutional generative adversarial network was introduced for data preprocessing to generate a pseudo-sample set with a similar distribution. Then, the fault sample set was input into Meta-DAE for adaptive feature extraction. Meta-DAE adopts a prototype domain enhancement strategy to make prototype points of the same category more closely clustered in the embedding space. At the same time, an encoder with noise reduction performance was constructed, and a target function based on prototype domain enhancement and denoising was designed. By fine-tuning the model under small-sample conditions, the noise robustness and classification accuracy of the model were improved. Experimental results of small-sample fault diagnosis under noise interference and variable operating conditions show that, compared to other models, the proposed model demonstrates strong noise robustness. Under -8 dB strong noise interference, the model achieves a classification accuracy improvement of 35.78% to 57.25% using only 10 samples for fine-tuning.