Pore-throat radius is one of the key parameters that characterizes the microscopic pore structure of rock, which has an important impact on oil-gas seepage and the prediction of remaining oil’s microscopic distribution. Currently, the quantitative characterization of a pore-throat radius mainly relies on rock-core experiments, then uses capillary pressure functions, e.g., the J-function, to predict the pore-throat radius of rocks which have not undergone core experiments. However, the prediction accuracy of the J-function struggles to meet the requirements of oil field development during a high water-cut stage. To solve this issue, in this study, based on core experimental data, we established a deep neural network (DNN) model to predict the maximum pore-throat radius R_max, median pore-throat radius R₅₀, and minimum flow pore-throat radius R_min of rocks for the first time. To improve the prediction accuracy of the pore-throat radius, the key components of the DNN are preferably selected and the hyperparameters are adjusted, respectively. To illustrate the effectiveness of the DNN model, core samples from Q Oilfield were selected as the case study. The results show that the evaluation metrics of the DNN notably outperform when compared to other mature machine learning methods and conventional J-function method; the root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are decreased by 14–57.8%, 32.4–64.3% and 13.5–48.9%, respectively, and the predicted values are closer to the true values of the pore-throat radius. This method provides a new perspective on predicting the pore-throat radius of rocks, and it is of great significance for predicting the dominant waterflow pathway and in-depth profile control optimization.