Abstract: Due to over-fitting of deep learning models brought on by the absence of picture data containing solar cell defects,few-shot defect identification has become challenging. As a method of enhancing the data,this study proposes employing meta-attention generative adversarial networks（MAGAN）,which incorporate meta-learning and dual-path attention. The dual-path attention module（DPAT）is designed to pay more attention to the small and faint defect features in the image during the feature extraction process. A clustering constrained loss function is proposed to solve the gradient disappearance problem during the training process while improving the network architecture. The meta-learning tuning module（MTM）is designed to optimize the weight parameters in the generator. The study and experiment discoveries demonstrate that the suggested strategy outperforms previous generative adversarial networks and may produce useful target datasets for modest sample faults.