Because of the anisotropy and non-uniformity, the grinding surface quality of Cf/SiC composites is difficult to be accurately predicted. To better predict the surface quality of 2.5D-Cf/SiC composites, the BP neural network was optimized by genetic algorithm (GA), and the surface roughness prediction model of fiber orientation, ultrasonic amplitude, and machining parameters was established. The empirical formula prediction model of surface roughness was established by the method of multiple linear regression analysis. The results show that the prediction accuracy of GA optimized BP neural network is the best, followed by the empirical formula, and the prediction effect of the BP neural network is the worst. The average absolute percentage error of these three models is 10.045 %, 16.912 % and 26.6245 % respectively. The kinematic models of conventional and two-dimensional ultrasonic-assisted grinding of single particles were established respectively. Based on the kinematic model, the reasons for the reduction of surface roughness by 2D ultrasonic-assisted grinding are explained. According to the orthogonal test results, the surface roughness decreases the most when vs is 0.66 m/s, vw is 100 mm/min, ap is 150 µm along the fiber orientation of 0°, and the maximum percentage of reduction is 53.18 %. Increasing the linear speed, reducing the feed speed, reducing the grinding depth, and applying ultrasound can reduce the extrusion pressure along the axis of 0° fiber, and then reduce the length and depth of cracks and thus reduce the surface defects. The axial shear force of the 90° oriented abrasive particles on the fiber is reduced, thus reducing the surface damage caused by torsional deformation. Reduce 3D surface profile and improve surface quality. The maximum percentage reduction of each parameter index is as follows: root mean square height decreased by 28.8 %, skew decreased by 88.61 %, kurtosis decreased by 48.97 %, maximum crest height decreased by 48.55 %, maximum sag height decreased by 40.09 %, maximum height decreased by 43.93 %, and arithmetic mean height decreased by 26.06 %.
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