To address issues of blurred skin lesion boundaries， hair interference， and varying lesion sizes， a skin lesion segmentation network based on dual-path attention mechanism and multi-scale information fusion was proposed. Firstly， a residual gated attention module based on depthwise separable convolution， named DGConv （Depthwise Gate Convolution）， was designed in the encoder to capture local lesion information. Secondly， a Multi-scale Contextual relationship Extraction Module （MCEM） was introduced at the bottleneck， which employed horizontal average pooling and vertical average pooling to model contextual information， and integrated multi-scale features captured by residual dilated convolution pyramid module to further enhance global lesion information understanding. Thirdly， a dual-path attention module was designed at the skip connections to refine lesion details， and a Multi-Scale feature Fusion Enhancement （MSFE） module was utilized to enrich feature details transmitted in current stage by fusing multi-stage information. Finally， a feature Fusion Module （FM） was designed in the decoder to solve receptive field mismatch problem at the same stage， progressively combining encoder outputs and feature information transferred by skip connection to achieve the final segmentation results. Experiments on the ISIC2017 （International Skin Imaging Collaboration） and ISIC2018 datasets demonstrated that the proposed network outperformed suboptimal networks in skin lesion segmentation， with the Dice improvements of 0.09 and 1.09 percentage points， respectively， and the Intersection over Union （IoU） improvements of 0.14 and 1.76 percentage points， respectively. Compared to the classical U-Net， the Dice was improved by 5.13 and 3.84 percentage points， respectively， and the IoU was improved by 7.74 and 6.04 percentage points， respectively， fully proving the advantage and effectiveness of the proposed network.