For the CT iterative reconstruction, choosing the parameters of different regularization terms has been a difficult problem. Transforming the reconstruction problem into constrained optimization can solve this problem, but determining the constraint range and accurately solving it remains a challenge. This paper proposes a CT reconstruction method based on constrained data fidelity term, which estimates the distribution of the constraint function by Taylor expansion to determine the constraint range. We respectively use Douglas-Rachford splitting (DRS) and Projection-based primal-dual algorithm (PPD) to split the reconstruction problem and solve the data fidelity subproblem. This method can accurately estimate the constrained range of data fidelity terms to ensure reconstruction accuracy and use different regularization terms for reconstruction without parameter adjustment. Three regularization terms are used for reconstruction experiments, and simulation results show that the proposed method can converge stably, and its reconstruction quality is better than the filtered back-projection.