Capsule networks can encode the properties and spatial relationships of skin cancer image features, thereby overcoming the disadvantage of information loss in the pooling process of convolutional neural networks. Aiming at the problem that only shallow features can be extracted and the convergence performance of the squash function in capsule networks, a ResNeXt cascaded with capsule networks was proposed for Rs-Capsnet networks. Firstly, the complex features of the image were learned using the ResNeXt network. The Inception module and the residual connection were used to extract the deep features, and the weights of the feature map were adjusted and delivered to the capsule module through the CBAM attention module. Then, an improved squash function capsule network was used to complete the classification. Finally, the improved network was compared with mainstream models. The results show that Rs-Capsnet exhibits better performance in skin cancer image classification.