The work aims to establish the removal rate model of deterministic shear thickening polishing (DSTP) based on BP neutral network by training the data of DSTP under different conditions, to provide basis for the deterministic removal control of material at polishing point. DSTP orthogonal experiment was conducted on BK7 flat glass, and the results were analyzed to compare the weight of each factor’s (polishing head speed, polishing gap, concentration of polishing slurry) impact on material removal rate of polishing point, to determine the input parameters of BP neural network. The number of hidden layer nodes was determined by experimental equation initially, and then the whole structure of network model was constructed by comparing the performance of the model under different hidden layer nodes. The final material removal rate model was established based on the experimental data designed network trained by the model. The comparison between the model prediction results and the experimental results showed that the relative error was less than 6.8% between the output of the established BP neural network prediction model and the measured results, proving the accuracy of the prediction model. The traditional theoretical model is difficult to accurately describe the complicated nonlinear mapping relationship between the process parameters of DSTP and the peak removal rate. The self-learning and adaptive ability of BP neural network can overcome this problem and provide a novel strategy for building the removal rate model of the deterministic shear thickening polishing process.