Objective: Aiming at the problems of unstable 3D printing quality and low printing efficiency of adipose tissue part of artificial meat based on plant raw materials, studied and improve the intelligent 3D printing trajectory motion algorithm matching with food materials, to reduce the accumulation and corners in the printing path. Methods: A trajectory planning method adapted to the characteristics of artificial meat adipose tissue raw materials was proposed. The complex section layer of the sliced model was divided into several simple polygon sub-partition contours using Bayesian algorithm, and an improved nearest neighbor method was used to determine the optimal motion starting point of each contour. At the same time, the genetic algorithm was integrated into the ant colony algorithm was applied to solve the optimal printing path of the partition contour, and finally the interior of the contour was filled with the improved zigzag scanning algorithm. Results: For the artificial meat adipose tissue in this study, through experimental verification, comparing the existing trajectory planning methods of Cura and Prusa, the established model was printed on a single-layer cross-section, and the printing time and motion path length were reduced, and the quality was good. Conclusion: The proposed trajectory planning algorithm for 3D printing of artificial meat adipose tissue has high feasibility and effectiveness.