Based on observed hydrometeorology data in the Xiangjiang River Basin from 1999 to 2013, the long short-term memory (LSTM) model and its variant models were used to study the influences of different input variables and different model structures with different foresight periods on runoff prediction results, and performance of the LSTM model and its variant models in short-term runoff prediction was evaluated. Then, the interpretability of the LSTM model for runoff prediction of the Xiangjiang River Basin was explored using the permutation importance and integrated gradient methods. The results demonstrate that incorporating hydrometeorological variable data with historical runoff data as the model input can significantly improve the prediction performance of the model, and changes in input variables have a greater impact on prediction results than differences in model structure; with the increase of the foresight period, the addition of precipitation data leads to different degrees of improvement in the model prediction performance, and the Nash-Sutcliffe efficiency coefficient (NSE) of prediction results increased by 2.0% with a foresight period of one day, while the increase in NSE was up to 13.6% with a foresight period of two to four days. Precipitation and historical runoff were found to play critical roles in runoff prediction, while the joint effect of early humid conditions and precipitation events was identified to be the primary factor inducing floods in the Xiangjiang River Basin. The LSTM model can reveal two distinct input-output relationships, corresponding to two flood-triggering mechanisms of recent rainfall and historical rainfall.