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Aiming at the problem of low prediction accuracy in river hydrology, a distributed automatic collection system for rainfall and hydrological information was designed using Internet of Things technology. A method for predicting river water level and runoff based on graph convolutional neural networks and Long Short-Term Memory (GCNN-LSTM) network models was proposed. Firstly, the main factors affecting river hydrology were identified through analysis, and the rainfall information within the watershed was composed into a grid based two-dimensional graphical matrix. Then, a GCNN-LSTM prediction model was proposed, using a two-dimensional graphical matrix containing rainfall information as input to the network model to obtain the spatiotemporal distribution characteristics of rainfall and hydrological changes in the watershed. Finally, the proposed GCNN-LSTM prediction model is used to train the historical hydrological data of the Yinghe River in Zhoukou City, Henan Province, then the trained network is utilized to predict the test set dataand and get a high-precision results of runoff and water level, and the RMSE, MAPE, and MAE of the runoff prediction results are only 17.09m3/s, 1.68%, and 8.57m3/s, respectively, while the RMSE, MAPE, and MAE of the water level prediction results were only 0.32m, 0.65%, and 0.29m, respectively, compared with other prediction methods, which demonstrates superiority and has a great significance for the scientific and rational utilization of water resources and flood control and disaster reduction.