The hydrological processes in karst watersheds are influenced by various factors, including climate characteristics, underlying surface properties, and human activities. Existing watershed hydrological models primarily rely on theoretical concepts or empirical function relationships for simulation, resulting in insufficient accuracy in hydrological process analysis for study areas with limited data. The structure of artificial neural networks is similar to the hydrological process structure in karst watersheds. Based on the hydrological characteristics of the Lijiang River, a BP neural network model is configured with structural parameters set to 13-9-1. Using hydrological data from the Lijiang River from 1995 to 2020 as the foundational dataset, the network is trained and tested for prediction accuracy. The results show that the coefficient of determination for the monthly runoff model in the Lijiang River basin, based on the BP neural network, is 0.942. This suggests that it is feasible to use historical data to predict future flow changes in the Lijiang River basin, assuming that the changes are due exclusively to precipitation and evapotranspiration, but no significant changes occur in the land uses. The findings hold significant importance for water resource management in typical karst watersheds.