In order to explore the application effect of different data-driven models in mid- and long-term runoff prediction.Taking the inflow runoff of Lianghekou Reservoir and Jinping First Class Reservoir in the Yalong River Basin as the research objects, the long-term and short-term memory network (LSTM) and BP neural network models were used to predict the annual and monthly runoff of each reservoir. A set of medium and long-term runoff prediction factors was constructed based on previous runoff information and circulation impact factor data. The parameters of Long Short Term Memory Network (LSTM) and BP neural network were optimized, and annual and monthly runoff prediction models for each reservoir were established.The prediction results show that the short-term and short-term memory neural network model (LSTM) has higher accuracy in predicting annual and monthly runoff than the BP neural network model, and both models have higher accuracy in predicting runoff in the Lianghekou Reservoir than in the Jinping First Class Reservoir. This research result can provide reference for mid- and long-term runoff prediction of large hydropower stations.