Recently, Salient Object Detection (SOD) is widely applied in numerous fields of computer vision, such as image recognition and segmentation. The existing SOD algorithms still have the problem of insufficient extraction of edge information. To address this issue, this paper proposes a salient object detection model utilizing edge feature fusion. Incorporating the feature extraction network, this model combines the attention mechanism and the edge feature extraction module, further improving the ability of the model to represent edge features. The Laplacian pyramid extracts salient edges of the image, connecting multiple feature extraction sub-modules through the side path to achieve the complementarity and fusion of salient target features and edge features. The channel and spatial attention modules form a series structure to conduct adaptive feature refinement for the fused features. The performance of the algorithm has been proved through experiments.