Abstract: This article presents a process parameter design method related to predicting the mechanical properties of 3D - printed TC4 titanium alloy based on the BP neural network method. In this paper, the author carried out research on the BP neural network algorithm for predicting 3D printing parameters specifically for parameters such as material yield strength, tensile strength, density, and 3D printing process parameters. It is concluded that the BP neural network algorithm can achieve good results with fewer data sets and can more accurately predict tasks with multiple feature values. This verifies the effectiveness of the BP neural network algorithm in predicting 3D printing parameters. The value and significance of this result lie in providing a more efficient and accurate method for predicting 3D printing parameters.