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In medical image segmentation, convolutional neural network (CNN) is affected by the diversity of skin lesions images, the location, shape and scale changes of segmentation targets, and other factors. A multi-dimensional attention module based on space, channel and scale is proposed to optimize the convolutional neural network image segmentation algorithm. Firstly, using the advantage of U-NET backbone network, its purpose is to make image feature extraction more perfect. Second, composed of multidimensional space, channels, scale attention mechanism identification of target lesion area detection, using the lower-level channel cascade from the encoder image features and the decoder senior image adaptive weighting fusion of attention unifies, enhance the awareness of the network on the sample lesions and discrimination, and highlight the most relevant characteristics of the channel, Emphasize the most salient feature maps between multiple scales. The segmentation test was carried out on ISIC2018 data set and images of different types of skin tumors provided by hospital plastic surgery department, and the index evaluation and comparison of different algorithms formed by random combination of attention modules showed that the average segmentation accuracy of the proposed algorithm could reach 92.89%. Experimental results show that the proposed algorithm is effective and feasible, and has higher robustness in the segmentation of cutaneous lesions under complex background.