Based on the sample data obtained from the bench test of a four-cylinder naturally aspirated CNG engine, three different neural networks, BP, SVM and GRNN, were used to develop the intake charge prediction model for the intake system of this engine, in which engine speed, intake manifold pressure and intake temperature, VVT angle and gas injection time were taken as input parameters and intake charge was used as output parameter. The comparative analysis of the experimental data and model prediction data showed that the mean absolute error (MAE) of BP model, SVM model, and GRNN model were 2.69, 5.13, and 8.11, and the root mean square error (MSE) were 3.53, 7.17, and 9.29, respectively. BP neural network has smaller prediction error and higher accuracy than SVM and GRNN neural network, which is more suitable for the prediction of the intake charge of this type of four-cylinder naturally aspirated CNG engine.