Abstract: By using data from Weining, Guiyang, and Sansui meteorological stations from 1990 to 1999, the effect of the approximation method in calculating the wet bulb temperature at different altitudes in Guizhou was evaluated, and the advantages and disadvantages of the BP neural network model (BPNN) and the approximation method in calculating the wet bulb temperature were compared. The results are as follows: (1) From the comparison of the wet bulb temperature between calculated by approximation method and observed at stations, the average absolute errors of the Weining, Guiyang, and Sansui stations were 0.059℃, 0.046℃ and 0.042℃, respectively. The proportions of data with errors less than 0.1 °C were 83.91%, 91.52%, and 92.76%, respectively. When the temperature was lower than 0℃, the frequency with the error greater than 0.2 ℃ showed an increasing trend, it is believed that there was a certain difference in the judgment of icing with the approximation method, which made the worse calculation at high altitude than that at low altitude. (2) Compared with the approximation method, the accuracy of BPNN for predicting wet bulb temperature was improved by 60.71%, 57.45%, 57.78%, respectively. The proportions of data with errors less than 0.1 °C increased to 97.38%, 97.18%, 97.44%, respectively. It effectively solved the problem of calculation error of approximation method caused by the high frequency of temperature below 0 ℃ in high altitude areas. The calculation results in low altitude from BPNN were also better than those from the approximation method. (3) The BPNN needed independent fitting for calculating the wet bulb temperature. The approximation method could be used when the requirement of the calculation accuracy was not high. Otherwise, a single station model should be established by the BP neural network.