In order to improve the accuracy of elastic-plastic analysis for SRC (steel reinforced concrete) beam-column members, the prediction model for fiber element parameters of SRC members (PMFEP-SRC) based on random forest algorithm is proposed to optimize the hysteresis curve fitting of SRC beam-column fiber elements. Based on 153 collected SRC beam-column specimens, with the peak load capacity ratio R_F and energy dissipation capacity ratio R_E are taken as target parameters, and the load capacity adjustment factor C_F and stiffness adjustment factor C_S are taken as adjustment parameters, the hill climbing algorithm is employed to determine the optimal parameters of the fiber element. PMFEP-SRC was trained and established by the random forest algorithm with the test control parameters of SRC beam-column specimens as the characteristic parameters and the optimal adjustment parameters of the fiber element (the solved load capacity adjustment factor C_F and stiffness adjustment factor C_S) as the labels. Finally, a batch of SRC columns with different shear-to-span ratios were designed and completed for low-cyclic loading tests, and the accuracy and reliability of PMFEP-SRC was further validated using the test data. The results show that PMFEP-SRC can effectively fit the hysteresis curves of SRC beam-column specimens with different failure modes, and the fitting accuracy of peak load capacity and energy dissipation of SRC specimens is significantly higher than that of the fiber elements without parameter optimization.