In order to solve the problem of time-consuming and labor-intensive process testing in the decision-making process of lapping/polishing, and estimate the process quality according to the process parameters in the lapping/polishing process. The BP neural network optimized by genetic algorithm was used as the main algorithm to construct the intelligent prediction model, and establish the mapping relationship between input parameters and output parameters in the lapping process. Then the effective input and output parameters were collected as sample data sets for network training and testing. The initialization weights and offsets of the neural network were optimized by genetic algorithm, and the neural network was trained with the sample data sets. Meanwhile, based on the theory of decision-making system, the neural network was combined with the decision-making system, and the learning ability of the neural network was used to build the database and rule base of intelligent decision-making, and finally the intelligent decision-making system was established. Compared with the decision-making method without improved BP neural network, the neural network performance optimized by genetic algorithm is better in both prediction accuracy and learning speed, but the decision-making system has better decision-making effect. It verifies the feasibility of the intelligent decision-making system of the lapping process and provides a new idea for the process decision of the lapping process.