Abstract: Heavy-duty CNC machine tools occupy an important position in the field of machining, and improving its reliability and machining accuracy is of great significance to the industrial development of China. Compared with conventional machine tools, heavy-duty CNC machine tools have the characteristics of complex structure, difficulty in fault tracing, few samples, and insufficient data, which make it difficult to conduct reliability research on them. Aiming at this problem, this paper uses the Weibull distribution of two parameters to establish the reliability model of the machine tool, introduces Bayesian theory to estimate its parameters, and calculates the parameter estimation results through the Markov Chain Monte Carlo method (MCMC). In order to improve the accuracy of parameter estimation, the traditional Bayesian method is improved, and the standard root mean square error value and confidence interval are used for evaluation and comparison. The results show that the improved Bayesian method has better parameter estimation accuracy and is more conducive to the establishment of machine tool reliability models.