Due to the rich colors and complex decorative patterns of Adelaide silk, it is difficult to segment its patterns, and it is prone to errors and omissions in segmentation. To this end, the work aims to propose an attention mechanism based segmentation algorithm for Adelaide silk decorative patterns. The FCN model was used for convolutional training of Adelaide silk decorative images, highlighting the semantic feature information of the images. By using the channel attention module and the position attention module, the Adelaide silk pattern images were respectively learned to obtain feature maps with identical dimensions. After fusing the feature maps of the two modules with the output image of the FCN model, the feature extraction results of the Adelaide silk pattern image were obtained. The regions of interest in the image were selected to complete the segmentation of the Adelaide silk pattern. The experimental results show that the proposed method has achieved high accuracy in segmentation results, with clear edges of the segmented image and no occurrence of wrong or missed segmentation. The overall segmentation results are relatively ideal.