Abstract: In the process of deep hole drilling of titanium alloy, due to its difficulty in processing, there are often problems such as serious tool wear, difficult chip removal and poor surface quality of the inner hole.In order to obtain titanium alloy deep hole parts with good bore surface quality and chip morphology, the new titanium alloy TC32 was taken as the research object, and the gullet-to-chip area ratio prediction and processing test verification of TC32 titanium alloy were carried out based on deep learning and BP neural network under different process parameters. The results indicate that the determination coefficient of the prediction model is 0.921, with a high degree of fitting and accuracy, and good prediction performance. When the feed rate is 0.08 mm/r and the spindle speed is 435 r/min, the gullet -to-chip area ratio is 5.6, The chip morphology is dominated by C-shaped chips and short strip chips,the chip removal smooth and the machining process stable.