Using Ant Colony Optimization (ACO) to mine rules is a research hotspot nowadays. This paper proposed a new zoning model of flood risk based on ant colony rule mining algorithm (Ant-Miner) to solve the non-linear relationship between index and flood risk grade. The model was used in the Beijiang River basin with the support of GIS technique. The assessment results show that ① 15 simple rules expressed in the form of conditional statement were mined by the AntMiner model. The rules are appropriate for the study areas and can be easily used for generating a zoning map of flood disaster risk. ② The test accuracy is 95.1% in the Ant-Miner model

92.9% in BP neural network model

indicating that the discriminative capability and flood risk zoning applicability of the former is stronger than the latter. ③ The high risk areas identified by Ant-Miner are mainly located in the regions with large precipitation

flat and low-lying terrain and dense population and property. These areas match well with the submerged areas of historical flood disasters

indicating that the AntMiner model is reasonable and practicable and can provide a new method for flood risk assessment.