The shape， color and texture of skin lesions are very different， and the boundaries are not clear， which makes it difficult for the traditional deep learning methods to segment them accurately. Based on the above challenge， this paper proposes a residual Inception and bidirectional convolutional gated recurrent unit （ConvGRU） empowered intelligent segmentation model for skin lesion. Specifically， a cloud-edge collaboration intelligent segmentation service network model for skin lesion is firstly designed. By this network model， users can obtain quick and accurate segmentation services. Furthermore， a novel intelligent segmentation model for skin lesion is developed. By integrating residual Inception and bidirectional ConvGRU， this model can fuse multi-scale features and make full use of the relationship between low-level features and semantic features. It improves the ability of the model to extract features and capture global context information， and leads to better segmentation performance. Finally， experimental results on ISIC 2018 dataset show that the proposed intelligent segmentation model achieves higher accuracy and Jaccard coefficient than several recently proposed U-Net extended models.