Abstract: Hail refers to the solid precipitation with a diameter greater than or equal to 5 mm caused by convection. Hail is also one main disastrous weather phenomenon in eastern Hubei, while Doppler weather radar is the most favorable tool for hail identification. At present, there are two hail identification methods used in the actual operation in eastern Hubei, one is artificial conceptual model, the other is the self-contained identification technology in short-time and proximity prediction system. The conceptual model needs to be judged by human, which is too subjective and threshold values of radar echo characteristic are not clear, while the false alarm rate(FAR) of existing automatic technologies in prediction system are too high. To overcome the shortcoming of the above methods, feasibilities of machine learning algorithms for hail identification are explored and a decision tree algorithm is established. Based on hail disaster data of Wuhan, Huanggang, Huangshi, Ezhou, Xianning and Xiaogan, Doppler weather radar data and convention high altitude sounding data of Wuhan from 2015 to 2021, the height of wet bulb 0℃(HWB0) and the height of wet bulb -20℃(HWB-20) are introduced into the hail identification factors, and artificial intelligence technology is applied in hail recognition. The performance is evaluated according to probability of detection(POD), FAR and critical success index(CSI). The result shows that both the decision tree algorithm with radar echo intensity (intensity decision tree) and the decision tree algorithm with radar echo intensity and wet bulb temperature height (intensity-height decision tree) can identify hail effectively. The POD results of the two decision tree algorithms are higher than 0.88, while the FAR are lower than 0.12, and the CSI are higher than 0.8, but the intensity-height decision tree performs better, with the POD and CSI increased by 5.68% and 7.5% than intensity decision tree respectively, while the FAR decrease 41.67%. The key factor of hail recognition by intensity decision tree is the combined reflectivity factor, and the bottom layer is the reflectivity factor of 0.5° and 1.5° elevation. The key factor of intensity-height decision tree is the reflectivity factor of 0.5° elevation and the judgment modules of radar echo extension height with the height of wet bulb temperature, especially with HWB0 included in the middle, and the bottom layer is the strength attributes of storm (vertically integrated liquid water and combined reflectivity). The analysis results of three cases with different occurrence time, location and hail size show that, due to the introduction of height of wet bulb temperature, the intensity-height decision tree reduces the number of empty alarm when the height of HWB0 and HWB-20 are high, especially when the HWB0 is high, thus reduces its FAR and improves its CSI, which indicate its potential wide prospect for operational application.