Abstract: Tikhonov’s regularization approach has been used to solve non-linear inverse heat conduction problems, using weighted Bregman distances in the construction of regularization terms for the Tikhonov’s function. Combined identifications can be achieved for non-linear inverse heat conduction with source term, thermal diffusivity and boundary conditions etc, facilitating the sensitivity analysis. Satisfactory numerical validation is performed including a preliminary investigation on the effect of noise data and the computational efficiency of different regularization terms. Results show that the proposed method can identify combined thermal parameters and boundary conditions for non-linear inverse heat conduction problems with high computational precision and anti-noisy capability. Moreover, the computational efficiency is improved with the weighted Bregman distances function as regularization terms.