In space missions，the separation performance of docking mechanisms directly affects the stability and reliability of the missions.In this paper，a digital twin experimental platform for space docking mechanisms is established based on the digital twin technology，along with the fusion method of data and knowledge.The Bayesian optimization algorithm is used to enhance the predictive model’s ability to learn the coupling relationship between the component degradation and separation performance，and a high-precision separation performance prediction model is established.The shapley additive explanations （SHAP） interpretability analysis is adopted to reveal the impacts of key components on the separation performance.The experimental results demonstrate that the platform improves the testing efficiency and reliability through real-time simulation，dynamic monitoring，and separation performance prediction，supporting the optimization and safety assurance of docking missions.