Canopy segmentation is a crucial step in obtaining canopy parameters in forestry remote sensing, and it holds significant importance for research areas such as forest carbon sequestration. However, canopy segmentation based on UAV imagery still faces challenges including confusion between edges and background, as well as insufficient edge details. To address these issues, this paper proposes a semantic segmentation network based on U-Net which implements boundary and key feature enhancement named BKFE-UNet, which is built upon the U-Net model and integrates a differential boundary attention module (DBM) and a key feature enhancement module (KFEM). The DBM enhances canopy edge information through differential computation, alleviating problems such as adhesion between adjacent canopy edges and inadequate edge details. The KFEM introduces Ghost convolution; by stacking Ghost features, it simultaneously filters out redundant information and enhances the key semantic features of canopy objects, thereby reducing background interference. On the UAV tree canopy segmentation dataset, BKFE-UNet achieves an mPA of 92.62%, an mIoU of 86.50%, an Accuracy of 96.30%, and an F1-score of 0.90, representing improvements of 2.23%, 2.27%, 2.09%, and 0.02, respectively, over the baseline U-Net, demonstrating a significant improvement over the baseline U-Net model.