Abstract：Probabilistic neural network (PNN) model is a kind of artificial neural network，which is simple in structure，easy for training and widely being used. The method uses Bayes classifying and decision-making theory to constitute the mathematic model of system；with Gauss function as activating one，it possesses the characteristics of strong nonlinear processing and anti-interfering ability. The theory and algorithm of PNN are expatiated，and then the application of PNN to rock slope stability forecasting is proposed. Immune evolutionary algorithm (IEA) that is an efficient random global optimization technique is used to optimize the parameter of Gauss function. The design idea and characteristics of IEA-PNN are introduced，and it is successful to apply this model to the rock slope stability forecasting. The results of case study show that the analysis results are completely consistent with the actual situation. It is shown that the IEA-PNN method is feasible in practice，it needs less learning sample，having more prediction-precision，stronger performance of dealing with non-linear dynamic data and better performance of non-linear system modeling than other artificial neural network methods and at the same time it provides a new approach for slope stability forecasting.
