The rockburst mechanism is very complicate

which is influenced by many factors. By the attribute reduction and the condition properties importance evaluating of rough set theory

it could determine the main factors of rockburst under specific geological conditions

and remove redundant data. Using genetic algorithm (GA) to optimize parameters of radial basis function (RBF) neural network

low-dimensional model inputs were transformed to high-dimensional space through hidden layer; the nonlinear relationship between impact factors and rockburst was fitted. Finally

a rockburst prediction model of GA-RBF neural network based on genetics of rough set theory was established; it had not yet seen the application to rockburst prediction. Based on several theoretical criteria according to the project

the established model was applied to rockburst prediction of practical engineering. It is approved that the evaluation results are consistent with the actual situation well; and it could play a very good guide for latter construction.