In the long-term operation of a central air conditioning system's mechanistic model, key physical parameters drift due to practical factors such as pipe scaling and equipment aging, leading to a gradual decline in model accuracy and severely impacting model-based optimization control effectiveness. To ensure the long-term reliability of the model and the effectiveness of control strategies, it is imperative to conduct in-depth performance analysis of the constructed physical model and effectively identify key parameters based on actual operational data.  This study builds upon the established mechanistic model of the central air conditioning system. Through sensitivity analysis, a critical parameter set significantly influencing system performance is extracted. An optimization function is established, aiming to minimize the total error of target variables such as cooling water inlet and outlet temperatures and EER. The parameter identification problem is transformed into an optimization problem, solved using approximate optimization methods, and validated with real engineering data.  Results demonstrate that this method significantly reduces errors in target variables. Taking EER as an example, the MAE before identification was 1.93, RMSE was 1.98, and MAPE reached as high as 61.4%. After identification, under single-target identification, MAE dropped to 0.008, RMSE decreased to 0.004, and MAPE fell to 0.29%. Under multi-target identification, MAE was reduced to 0.28, RMSE to 0.51, and MAPE to 9.74%. The validation results confirm the method's effectiveness in improving model accuracy and engineering applicability.