In the uncertainty evaluation of wind speed measured by wind speed sensors, the traditional method is to simplify the measured wind speed model and use guide to the expression uncertainty in measurement(GUM) to evaluate. However, GUM is not suitable for complex model. In order to study reliable method for the uncertainty evaluation of measured wind speed, GUM and Monte Carlo method (MCM) were used for uncertainty evaluation. On the basis of comparative analysis of results, the applicability of GUM was verified using MCM evaluation. The results show that under the simplified model, the difference between GUM and MCM evaluation is small, but only when the standard uncertainty is taken as one significant digit, GUM evaluation method is verified and evaluation is consistent; MCM evaluation under the actual measurement model are similar in envelope shape compared to GUM evaluation under simplified model, but the best estimate of measured wind speed is significantly larger, GUM evaluation method cannot be validated; When changing the distribution of some input variables, the two methods evaluated best estimated values of measured wind speed are very close. However, the inclusion interval of GUM evaluation is significantly wider than that of MCM, the probability distribution difference is significant, and GUM evaluation method cannot be validated. Therefore, appropriate evaluation methods should be selected according to the complexity of model, the distribution of input quantities and accuracy of measurement. If the distribution of input quantities follows normal distribution and measurement accuracy is not high, GUM can be used for evaluation. On the contrary, MCM is recommended to evaluate to improve the accuracy and reliability of observation results.