Abstract: Long Short-Term Memory(LSTM) neural network model, a deep learning method with strong capability of temporal series analysis, has unique advantages in runoff prediction.However, the optimal scheme of input and output lengths in this model is still not clear, so it is of practical significance to explore the influence of different input and output lengths on daily runoff prediction efficiency by LSTM.Taking Dadu River, Yalong River, tributaries of Minjiang River and upper reaches of Jialing River in western Sichuan Province as the study area, 20 sub-catchments were selected to test the daily runoff in different forecast periods with input data of different lengths and the daily runoff prediction efficiency of LSTM model under different data lengths was studied.The applicability of this method in different river basins and the characteristics of optimal input and output length were analyzed.The results show that:(1) When the previous precipitation, temperature and runoff are taken as inputs, the input length has little effect on daily runoff prediction, but the accuracy will decrease with the extension of forecast period.Therefore, the forecast period should be set within 7 days to guarantee forecast accuracy.(2) When only previous precipitation and temperature are taken as inputs, the forecasting accuracy will increase with the extension of previous data and decrease with the extension of forecast period.Therefore, the length of preliminary data should be beyond 7 days, and the forecast period is preferably 1 day, and should not exceed 3 days.(3) Runoff variability is an important indicator affecting the prediction efficiency and the optimal combination of input and output lengths.The prediction results in catchments with strong variability show low accuracy and weak sensitivity to input and output lengths.The research results can provide a reference for improving the runoff prediction efficiency by deep learning method, and help to determine the suitable input and output length combination scheme in consideration of watershed characteristics.