Thin-walled blades are characterized by complex structures, low rigidity, and challenging material processing. During the manufacturing process, they are subjected to deformation caused by various sources such as tool wear and machine tool accuracy evolution, causing poor machining precision and long production time. To address this issue, a modeling and compensation method of machining errors for thin-walled blades was proposed, in which a machine learning algorithm was adopted to establish the machining error model. Meanwhile, a real-time error compensation system based on the external machine zero-point shift function of the NC system was developed to offset the machining error. Finally, a milling experiment for thin-walled blades was conducted to verify the proposed method. The experiment results revealed that the machining errors, including both elastic deformation and plastic deformation, were significantly reduced after the machine measurement and compensation process, effectively improving the machining accuracy.