Abstract: Riedel--Hiermaier--Thoma (RHT) constitutive model is extensively employed in tunnel blasting and impact-resistant structural design. However, experimental calibration of specific parameters is impeded by prohibitive costs, often necessitating trial-and-error adjustments that undermine modeling efficiency and simulation accuracy. To overcome this limitation, an efficient and robust inverse identification framework is developed for 16 difficult-to-calibrate RHT parameters by integrating PAWN global sensitivity analysis with intelligent optimization algorithms. Leveraging a MATLAB--ANSYS/LS-DYNA co-simulation platform, the area difference A_D of stress--strain curves is introduced as the core evaluation metric. Results reveal that only 8 of the 16 parameters significantly affect the model response. The proposed methodology achieves relative inversion errors ranging from 0.23% to 9.28%, with its reliability rigorously validated through semicircular bend Split Hopkinson Pressure Bar (SCB-SHPB) tests and scaled blasting experiments. This approach markedly improves both the accuracy and efficiency of parameter identification, demonstrating strong engineering applicability.