This article explores the intelligent diagnosis technology for skin cancer based on residual neural networks. By constructing corresponding models and utilizing deep learning technology to extract effective features from a large number of skin images, the accuracy and efficiency of diagnosis are improved. ResNet solves the problems of traditional CNN models by introducing residual blocks and skip connections, increasing the depth and expressive power of the network. The experimental results show that the intelligent diagnostic technology based on ResNet can effectively identify skin cancer with high accuracy, and can provide auxiliary diagnostic assistance for doctors. This technology is expected to reduce the difficulty of diagnosing skin cancer, improve the accuracy and efficiency of diagnosis, and provide better support for the treatment of skin cancer.