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Automated skin disease classification is crucial for the timely diagnosis of skin lesions. However, accurate skin disease classification presents a challenge, given the significant intra-class variation and inter-class similarity among different kinds of skin diseases. Previous studies have attempted to address this issue by identifying the most discriminative part of a lesion, but they tend to overlook the interactions between multi-scale features. In this paper, we propose a Progressive Multi-stage Attention Network (PMANet) to enhance the learning of multi-scale discriminative features, so that the model can gradually localize from stable fine-grained to coarse-grained regions in order to improve the accuracy of disease classification. Specifically, we utilize a progressive multi-stage network to supervise feature and classification, thereby fostering multi-scale information and improving the model's ability to learn intra-class consistent information. Additionally, we propose an enhanced region proposal block that highlights key discriminative features and suppresses background noise of lesions, reinforcing the learning of inter-class discriminative features. Furthermore, we propose a multi-branch feature fusion block that effectively fuses multi-scale lesion features from different stages. Comprehensive experiments conducted on two datasets substantiate the effectiveness and superiority of the proposed method in accurately classifying skin disease. • Progressive training strategy optimizes model for multi-scale feature interactions. • Proposed supervised constraint model learn intra-class semantic feature efficiently. • Enhanced Region Proposal block learn key feature while suppressing background noise. • The Multi-Branch Feature Fusion block fuses features from different stages. [ABSTRACT FROM AUTHOR]