Abstract: In virtual flight testing and aircraft design, high-dimensional and high-fidelity computations frequently entail significant computational expenses. Surrogate models are a useful technical approach that can significantly improve the computational efficiency, while possessing the capability to fuse multi-source and multi-fidelity data, and can be used for aerodynamic performances correction based on sparse experimental data points. To this end, the governing equations are projected into a lower-dimensional space, and an intrusive reduced-order boundary layer correction algorithm is proposed to efficiently expand the sampling database while ensuring accuracy. The surrogate model constructed on this basis can enhance the prediction accuracy of the aircraft drag coefficient. Furthermore, by fusing multiple sources of sparse experimental data into the reduced-order model and introducing boundary constraints based on the least squares method, the surrogate model’s prediction of surface pressure distribution performances is made more consistent with the given experimental data. Subsequently, a Kriging bridge function is constructed to extrapolate local constraints to entire surface, enabling intelligent matching of correction values for pressure and heat flux distribution at any position on the surface. This provides a new algorithm for the fusion of multiple sparse constraints. The method described above has been validated in two-dimensional airfoil, three-dimensional blunt cone, and tailless aircraft cases. The results indicate that the surrogate model constructed by the multi-fidelity data fusion algorithm predicts smaller residuals in the flow field and provides more accurate predictions of the drag coefficient compared with a surrogate model using single-fidelity sources. Moreover, when there is sufficient flow field information at the outer edge of the boundary layer, the predictions from the multi-fidelity surrogate model closely resemble the results obtained from computational fluid dynamics calculations. The further developed multi-source data fusion algorithm and the intelligent aerodynamic characteristics extrapolation algorithm can effectively fuse experimental data points, improving the prediction results of surface pressure and heat flux distribution.