Due to the complexity of the aerodynamic environment， there are many uncertainties in the aerodynamic evaluation of rotor blades， which have a significant impact on their performance. The existing aerodynamic evaluation methods cannot take into account the uncertainty of aerodynamic data. This study applies the data fusion technology to the prediction of rotor blade aerodynamics. The aerodynamic force distribution and confidence intervals with higher credibility under the conditions of remembering certain uncertain factors are obtained， laying a foundation for further uncertainty analysis and engineering applications of rotor blade aerodynamic data. Based on the hypothesis that the aerodynamic force data from different sources are all normally distributed independent random variables， the relationship between the distributed aerodynamic data and measured values is used as the fusion criterion to achieve the best matching of the measured values. The Bayesian estimation is used to solve the maximum posterior probability distribution of the fused data， thus constructing an aerodynamic data fusion method based on the Bayesian framework. Using the UH-60A and Caradonna-Tung rotor blades as examples， the proposed method is used to fuse the aerodynamic data from different sources， and the estimation variance and prediction error of the fused results are analyzed and compared. The results show that firstly， the proposed method， without special requirements for the source of input data， can provide confidence intervals for fused results， and reduce the estimation variance of data from different sources. On this basis， uncertainty analysis can be conducted. Secondly， the fused results are not limited to data from different sources， and thus broaden the coverage of data. Finally， the fused results are more in line with physical laws than the data from a single source.