Parameters identification of rheological constitutive model is an important content of geological rheology. On the basis of the in-situ measuring displacement information of the test cavity surrounding rock of some hydroelectric station underground powerhouse, rockmass rheological parameters are identified through 3D viscoelastic-plastic numerical analysis and intelligence method that include uniform design (UD) ，BP artificial neural network (BP-ANN) and genetic algorithm (GA). And then the surrounding rock mass rheological displacements as a result of the excavation of test cavity are forecasted. In comparison with the monitoring displacements, the forecasting displacements and velocities are approximate to the measured displacements and velocities, which prove the rightness and reliability of the above method.