Abstract: With the availability of a large number of sharing models, model search and task design would be an extremely complex project in the global navigation satellite system (GNSS) -distributed simulation environment (GDSE). For improving the efficiency of model search and task design, a real-time intelligent recommendation method was designed for GDSE. Based on the characteristics of the simulation model, the incidence relation and interface shape of the model were defined in the method and a conditional frequent pattern tree (FP-tree) structure was designed to further improve the retrieval efficiency. The effect of the conditional FP-tree structure was proved theoretically. Then, the calculation method of the model incidence relation degree was proposed and derived based on the Bayesian statistical method. The entire processing of the intelligent recommendation method was designed for implementing it in GDSE. Hence, to check the effect of the real-time intelligent recommendation method, it was implemented in GDSE. Compared with the simulation result of the traditional recommendation method, the model intelligent recommendation method is proved to have a shorter running time and a high accuracy on simulation model recommendation. The computing capability and real-time performance are proved through the simulation. It is demonstrated that the intelligent recommendation method is efficient and flexible for GDSE.