The task of camouflaged object segmentation is to accurately classify and localize objects that are highly similar to the background using pixel-level segmentation masks, which is more challenging than traditional object segmentation tasks. Aiming at the problems that the target is highly similar to the surrounding environment, the boundary is blurred, and the contrast is low, a camouflaged target segmentation method based on edge enhancement and feature fusion is constructed. First, a set of edge extraction modules is designed, aiming to accurately segment valid edge priors. Afterwards, a multi-scale feature enhancement module and a cross-level feature aggregation module are introduced to mine multi-scale contextual information within and between layers, respectively. In addition, a simple inter-layer attention module is proposed to effectively filter out the interference information existing after fusion by utilizing the difference between adjacent layers. Finally, accurate prediction results are obtained by combining feature maps of all levels with edge priors step by step. Experimental results show that the model outperforms other algorithms on four camouflaged target benchmark datasets. Among them, the weighted F value increased by 2.4%, the average absolute error decreased by 7.2%, and the segmentation speed reached 44.2 FPS under the RTX 2080Ti hardware environment. Compared with existing methods, this algorithm can segment camouflage targets more accurately.