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In numerous fields, infrared thermometry technology is widely utilized due to its advantages, such as non-contact capability, long-distance range, and high sensitivity. However, the precision of infrared thermometry is affected by environmental factors and emissivity, which restricts the development of this technology. To enhance the accuracy of infrared thermometry, this study proposes a method to improve the precision based on the principles of Wiener transformation and Bayesian parameter estimation theory. Firstly, a Wiener infrared temperature variation model with random mixing effects is established based on the theory of infrared thermometry. Secondly, to address the issue of parameter estimation in the model, a three-step iterative algorithm is designed using the mean-field approximation theory to compute the model parameters. Then, the model is solved using the calculated parameters to obtain the target temperature. Finally, the feasibility of the proposed method is verified through numerical simulations and temperature measurement experiments on aluminum alloy rings