Based on orthogonal experimental results of porous Si3N4 ceramics by gel casting preparation, a three-layer back propagation (BP) artificial neural network (BP ANN) was developed for prediction of the flexural strength and porosity. The BP ANN is composed of three neurons in the input layer, two neurons in the output layer and six neurons the hidden layer. This study demonstrates that the proposed neural network approach can predict the performances of porous Si3N4 ceramics by gel casting preparation to a high degree of accuracy, and the neural network is a very useful and accurate tool for performances analysis of porous Si3N4 ceramics. By the proposed neural network prediction and analysis, the results suggest that the porosity monotonically decreases with the increase of solid loading, flexural strength is low when solid loading was too low or too high, and flexural strength has an optimum value.