This paper used a maximum entropy theory to establish a general probability distribution model for tree measurement parameters. This model has an explicit explanatory expression and overcomes some problems occurred in the traditional methods that the reasons why the tree measurement parameters obeyed certain probability distribution cannot effectively be explained. Therefore, this model provides a new way to establish the statistical distribution for tree measurement parameters. The established general probability distribution model was used to simulate the Moso bamboo′s diameter distribution in Zhejiang province based on 1-3, 1-4, and 1-5 stage sample moments, respectively. The results indicated that using 1-4 stage sample moment provided the best simulation performance, and provided even better effects than that using Weibull distribution. Both maximum entropy theory and Weibull distribution have similar features that can effectively simulate the reference data. The sum of square deviation is 0.00018 based on maximum entropy theory and 000045 based on Weibull distribution. Because different system and non-system factors can affect the reliability of the estimates, the established models was used to evaluate the measurement uncertainty, indicating the reliable results with estimates of 7.85100, standard uncertainty of 1.8271, and confidence probability of 0.9602.