Titanium alloys have good mechanical properties and organizational stability. However, due to the larger viscousity of titanium, a reasonable choice of the characteristic parameters of oilstone will directly affect the quality and efficiency of honing processing. This article solved multi-objective problem using artificial neural network with fast convergence and high precision. Based on a comprehensive analysis of the relationship between the workpiece material, materials status, surface hardness, the required surface quality and various parameters of oilstone, the improved artificial neural network algorithm-GCAQBP was adopted, through coding optimization of input and output parameters, model of intelligent choice of oilstone’s parameters was constructed about titanium alloy cylinder honing processing. Through experimental studies, it is shown that the intelligent model can choose quickly with high reliability compared with the traditional experience.