The prediction of impurity disruption during the discharge period of experimental advanced superconducting tokamak (EAST) is of great significance for the long-pulse steady-state discharge of future EAST. According to the physical characteristics of impurity disruption, the data of 334 impurity disruptive discharges in 2018 and 1 628 non-disruptive discharges in 2021 are selected as training discharges. Then, the training samples composed of eight diagnostic signals, including plasma equilibrium, density, current, and radiation signals, are used to train the impurity disruption prediction model by LightGBM. The test results reveal that the LightGBM model can accurately predict the impurity disruption, with a success rate of 96.29%, while for non-disruptive discharges, the false positive rate is 6.87%. The research results indicate that it is feasible to use LightGBM to predict plasma impurity disruption of EAST.