Abstract: Identifying the surface aerodynamic heat flux using the time-series temperature data collected from internal measurement points of the thermal protection system is a crucial method for acquiring the surface thermal environment of high-speed aircraft. Analyzing and quantifying the uncertainty of identification results can provide a quantitative criterion for the validity of test data and support the safety assessment of “ extreme thermal service conditions" in thermal environments. Surface heat flux identification is a typical ill-posed problem, and measurement noise can lead to explosion in high frequency, so regularization is needed to obtain stable solution. Regularization reduces the uncertainty of estimation results, but it also brings obstacles to uncertainty quantification analysis. Therefore, regularization is regarded as a low-pass filter, and the identification results are transformed into the frequency domain to analyze the uncertainty of identification results. Finally, the uncertainty sources of typical aerospace surface heat flux sensor are analyzed. The effectiveness of uncertainty quantification is verified by Monte Carlo method.