In view of the large amount of calculation of Markov Chain Monte Carlo (MCMC) algorithm and the serious noise in the statistical results, an X-ray image reconstruction method based on a hierarchical model and low-rank approximation is proposed. First, a total variation (TV) regular term is introduced to construct the objective function, and hyperparameters are defined based on Jeffreys prior to establish a hierarchical Bayesian model. Then, the variable split method is used to obtain the conditional probability density distribution of each variable in the split form. Finally, according to the low-rank nature of the forward model, the objective distribution function of the low-rank approximation is calculated, so as to obtain the closed solution of the parameters to be sought. The results show that the proposed method can effectively solve the large amount of calculation in the Bayesian inverse problem. Compared with the existing reconstruction methods based on uncertainty quantification, the proposed method can effectively suppress the image noise while retaining the edge details of the image better.