Abstract: At present, the roughness of cutting surface needs to be combined with manual experience and multiple testing methods, and the machining quality is difficult to be guaranteed. On the basis of giving full play to the role of historical parameters in machining stage, a wear monitoring model was established. At the same time, in order to meet the requirements of the algorithm accuracy and response rate, we introduced the adaptive generalized regression neural network (AGRNN) for roughness prediction. The results show that the correlation coefficient between the calculated roughness prediction data and the actual value reaches R²=0.988, the prediction model reaches the ideal control state, the prediction accuracy meets the control standard, and the response time can be further shortened after the equipment adjustment. Spindle speed 1000～2000 r/min, feed 0.2～0.3 mm/r, axial cutting depth 0.2～0.4 mm, radial cutting depth 1～5 mm range, AGRNN corresponding wear and roughness MAPE of 3.685 and 2.236 in turn, It is lower than the four algorithms of convolutional neural network (CNN), Gaussian process regression (GPR), support vector machine (SVM) and multiple linear regression (MLR), achieving the ideal prediction effect and significantly shortening the control decision time.