To address problems in foreign object occlusion， a lack of feature information， and the incorrect segmentation of lesion areas during skin lesion image segmentation， a skin lesion segmentation method based on a high-resolution composite network is proposed. First， we use a preprocessing operation to refine and expand the skin lesion image to reduce the impact of foreign object occlusion on the network segmentation performance. Subsequently， we use a high-resolution network and multi-scale dense module to construct the encoding part. The high-resolution network can ensure the global transmission of high-definition feature maps， and the multi-scale dense module can maximize the transmission of lesion features， reduce the loss of image feature information， and accurately locate skin lesion areas. Next， we use a reverse high-resolution network and double residual module to construct the decoding part. The double residual module can capture deep semantic information and spatial information when reconstructing decoding features and improve the segmentation accuracy of skin lesions images. Experiments are performed on the ISBI2016， ISBI2017， and ISIC2018 datasets， whereby the obtained accuracies are 96.14 %， 93.72 %， and 95.73 %， respectively； the Dice similarity coefficients are 93.16 %， 88.56 %， and 92.00 %， respectively； and the Jaccard indices are 87.01 %， 77.19 %， and 85.19 %， respectively， and the overall performance of the segmentation method is superior to existing methods. Simulation experiments reveal that the high-resolution composite network demonstrates a superior segmentation effect on skin lesions images， which opens new avenues for the diagnosis of skin diseases.