Abstract: Aiming at the problem that the micro-milling of titanium alloy is prone to produce burr defects that affect the use, a prediction method of the burr size at the top of Ti-6Al-4V micro-milling based on depth transfer learning was proposed. Firstly, a prediction model of micro-milling burr size was established with the process parameters (spindle speed, axial cutting depth, radial cutting width and feed per tooth) as the network input and the burr length as the prediction target. Secondly, 625 cutting simulation samples were used for pre-training. Finally, based on the transfer learning mechanism, 100 cutting experimental samples were used to fine-tune the pre-training results, so as to transfer the simulation law to the experimental law. The results show that the average prediction accuracy of the transfer learning model for the burr size on both sides of forward and backward milling is 95.77% and 95.45%, which provides an effective method for the prediction and control of titanium alloy micro-milling burr.