As critical components of aero-engines, turbine blades are prone to defects such as cracks, burns after long-term service, which can directly affect the safe and efficient operation of aircraft. To address the limitations of conventional machine vision or semantic segmentation methods in accurately segmenting blade defects under complex conditions, this paper proposes a Swin-DCUnet-based segmentation and assessment method for aero-engine turbine blade surface defects. The core of this approach is the semantic segmentation model Swin-DCUnet, which employs the Swin Transformer—capable of extracting multi-scale features—as the backbone feature extractor. The extracted features are fused through a dual-channel convolutional process, and a hybrid loss function is introduced to improve model convergence speed and segmentation accuracy. Furthermore, a defect severity grading method is developed by integrating the predicted segmentation results with quantitative analysis, providing a valuable reference for subsequent blade maintenance. A dedicated dataset and evaluation metrics are constructed, and ablation experiments are conducted. Results show that the proposed Swin-DCUnet achieves AR, AF1, mIoU, and Dice scores of 92.18%, 92.92%, 87.44%, and 47.85%, respectively, demonstrating its advancement, effectiveness, and practicality.