Objective To explore the application of three parameter identification methods (impedance modulus curve method, impedance component method, and genetic algorithm) in solving parameter identification problem of the 11-element lumped parameter model in the circle of Willis. Methods Using the flow and pressure waveforms of the internal carotid arteries and vertebral arteries on both sides as inlet conditions, parameter values of the model under normal and bilateral vertebral artery stenosis conditions were calculated. The recognition algorithm was verified by using Simulink models, and finally the stability of the recognition algorithm was verified by adding a certain noise to the flow. Results Under normal circumstances, the proximal resistances obtained by the impedance modulus curve method were larger, and the resistances of the anterior communicating artery obtained by the impedance component method were larger. The genetic algorithm could obtain relatively reasonable model parameter values. In the case of vertebral artery stenosis on both sides, the impedance modulus curve method could obviously get the results of the increasement in proximal resistances of the posterior circulation, but the results obtained by the impedance component method and the genetic algorithm mainly lied in that the distal resistance had a larger increase. Conclusions There are still differences between the pressure data calculated by the parameters identified by the above three methods and the actual data, which are considered as modeling errors, source data errors and calculation errors. The impedance modulus curve method has a certain effect in distinguishing changes of the proximal and distal resistances, but there exist large errors in identification of some parameters. The impedance component method can identify the parameters, but this method is unstable with large calculation errors. Genetic algorithm can obtain a better approximate solution, but it has certain problems in distinguishing vertebral artery stenosis. The combination of impedance modulus curve method and genetic algorithm may play a better role in future application of this model for disease diagnosis.