Air-based BIPV/T is of significant research interest in reducing energy load and improving indoor comfort. As many factors related to meteorology, geometry and operation contribute to the thermal performance of BIPV/T, especially for one kind of hybrid air-based BIPV/T (HAB-BIPV/T), quantifying the effects of such uncertain parties is essential. In this paper, a numerical analysis was conducted regarding 13 parameters of one HAB-BIPV/T prototype. For each quantity of interest, the kernel density estimate was regarded as an approximation to the probability density function to assess uncertainty propagation. A sequential sensitivity analysis was used to quickly screen (by Morris) and exactly quantify (by Sobol’) the effects of significant variables. The surrogate model based on a back propagation neural network was employed to dramatically reduce the computational cost of Monte Carlo analysis. The results show that the uncertain inputs discussed can induce considerable fluctuations in the three quantities of interest. The most significant parameters on AUI include air inlet height, cavity thickness, air inlet velocity and number of air inlets. The outcomes of this study provide insights into the correlation between various factors and the thermal efficiency of the HAB-BIPV/T as a reference for similar design works.