In this paper, a decomposition based recursive finite-data-window least squares identification method with a forgetting factor is proposed for Hammerstein systems. The proposed method aims to identify the parameters of Hammerstein systems by decomposing them into two subsystems, one involving linear subsystem parameters, and the other containing the nonlinear subsystem parameters. To achieve this, a two-step finite-data-window recursive least squares method with a forgetting factor is developed. To verify the effectiveness and merits of the proposed algorithm, a simulation example is provided, demonstrating that the proposed algorithm can quickly track parameters and accurately and effectively identify Hammerstein systems.