The indoor airborne pollutant has a great impact on indoor air quality. During the calculation process of pollutant distribution, some difficult-to-obtain parameters are critical in ensuring the calculation accuracy. In this study, we proposed a hybrid data assimilation method combined with Ensemble Kalman Filter (EnKF) and Gibbs sampling to reconstruct airflow path parameters (flow coefficient and flow index) in a multi-zone building model. Specially, we built a scaled multi-zone building model and used CO2 as the tracer gas to simulate the pollutant distribution in the building. The monitored sensor concentration data were used as prior information to reconstruct airflow path parameters of the scaled building model. And the performances of different data assimilation methods were compared. The results show that the EnKF method can reduce the calculation time by 46 % compared to the Gibbs sampling method, but the predicted accuracy decreases by 38 %. However, when we used the hybrid algorithm to reconstruct the airflow path parameters, the calculation time was reduced by 67 % compared to the EnKF method, and the predicted reconstruction accuracy is 7 % higher than the Gibbs sampling method.