For industrial process transfer functions, this paper proposes a parameter estimation method combining the statistical identification method and the nonlinear optimization strategy. In order to obtain enough system modal information, a moving data window is used to deduce the identification method. Based on the dynamical measured data, a stochastic gradient parameter estimation algorithm is presented. In order to improve the estimation accuracy, a multi-innovation stochastic gradient parameter identification method is proposed for transfer functions by expanding the innovation scalar into the innovation vector. Finally, a simulation example is given to compare the proposed methods. Meanwhile the performance of the proposed algorithms is analyzed and the estimated models are verified by step response tests.