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The integration of prior knowledge into the deep network is a critical step in addressing the challenges of overfitting and data imbalance in skin disease classification. The FFTL-Net achieved AUC value of 98.16% on the International skin imaging collaboration (ISIC) 2018 dataset and 98.31% on the ISIC 2019 dataset. This represents an improvement of 1.25% and 0.33% compared to the second-ranked algorithm, respectively. The experimental results demonstrate the efficacy of the model in addressing the data imbalance issue in skin disease datasets, with prediction accuracies of at least 93% being achieved for BCC and other rare samples. 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