Due to the long-tailed distribution characteristics of skin cancer data, quickly identifying rare skin disease samples with small amounts of data becomes a challenging few-shot problem. The detection method based on meta-learning can quickly learn meta-knowledge from the majority of common classes skin disease classes and use prior knowledge to improve the model's ability to detect rare skin diseases. However, the bias in the quality and distribution of public skin cancer categories leads to the risk of overfitting in the pre-training phase of meta-learning, and meta-learning models based on traditional networks struggle to handle fine-grained skin disease problems. Affronting this issue, a many-objective meta-learning skin cancer detection model is proposed. The proposed model optimizes the distribution of common classes(base classes) by considering various classification performances of the skin cancer detection model, thus obtaining quality-enhanced training samples. And it adopts the ResNet12 network structure integrated with the CCNet attention model, which can significantly enhance the ability to identify fine-grained skin lesion images. Additionally,an improved many-objective optimization algorithm with discrete grouping strategy is designed to efficiently solve the proposed model. Some experiments are conducted on two public medical datasets, ISCI2018 and Derm7pt. The proposed many-objective skin detection model achieves detection accuracies of 67%, 79%, and 82% respectively on binary classification tasks with 1-shot, 3-shot, and 5-shot learning,which validates the effectiveness of the model.