This paper proposes a switched Hammerstein model identification method based on special historical data segment mining. Special data segment refers to data in stable state and stable slope response. First, a random sampling consensus algorithm is used to identify the static nonlinear subsystem based on the steadystate data. Secondly, based on the stable slope response data, the density peak clustering algorithm is used to identify the dynamic subsystem structure and the corresponding operation interval. Finally, the least squares algorithm is used to identify the model parameters of the switched dynamic subsystems based on the data sets in the operation interval. The results of numerical simulation and experimental cases show that, compared with the standard Hammerstein identification method, the proposed method can realize the structure identification and operation interval division of multiple linear dynamic submodules with different switching points, reduce the influence of switching dynamic subsystems on parameter identification when the model structure is unknown, and improve the identification accuracy of switching Hammerstein models.