Abstract: Simulation test apparatus of oxidation and self-heating of sulfide ore heap has been designed independently to obtain the change law of the oxidation and self-heating temperature of sulfide ore heap.In the tests，the sulfur content，ore fragmentation，temperature gradient are taken into account as main influence factors，and the oxidation and self-heating temperature rise rate of sulfide ore heap as a test indicator，L9(34) orthogonal table was used to establish the orthogonal regression test of three factors and three levels.GRNN neural network model was established to predict oxidation and self-heating temperature of sulfide ore heap by using MATLAB.K-fold cross validation is applied to GRNN neural network to obtain optimum smoothing factor σe.The RBF neural network model，gray neural network model to predict effects were compared with that of GRNN model predictions.The results show that GRNN neural network has the advantages of network approximation ability，converged speed，and the stability of the algorithm in prediction model of few observations.Prediction accuracy of GRNN model of the oxidation and self-heating temperature of sulfide ore heap is high with the prediction error of 3.51%.

Key words: Sulfide ores, Oxidation and self-heating temperature, Temperature rise rate, Prediction model of few observations, GRNN neural network