The Three Gorges Reservoir operates at a low water level of 145 m during flood season and a high water level of 175 m during the non-flood season for the purposes of flood control, power generation and shipping. According to the changes of the water level, the operation period of the reservoir can be divided into four stages, which are descent stage (175-145 m), low level stage (145 m), ascent stage (145-175 m) and high level stage (175 m). In this study, the outflow water quality in different water levels of the Three Gorges Reservoir was evaluated by using comprehensive water quality identification indicator based on the weekly water quality monitoring data of water quality indicators (DO, CODMn and NH3—N) from 2011 to 2018, and a classical time series autoregressive integrated moving average(ARIMA) model was used to predict future water quality changing trends. The objective of this research is to study the influence of water level scheduling of the Three Gorges Reservoir on the outflow water quality and its response to the changes of water level, so as to predict water quality change tendency. The results showed that the seasonal water level scheduling of the reservoir had a significant influence on the outflow water quality. The concentration of the water quality indicators (DO、CODMn、NH3—N) presented cyclical changes in response to different operation stages. The concentration of DO was much higher at high level stage than that at low level stage, whereas the concentration of CODMn and NH3—N was the opposite. Moreover, Pi at different stages all maintained ClassⅠstandard of comprehensive water quality, with the best water quality occurring at high level stage and worst at low level stage. Predicting outcomes of ARIMA model show that the water quality will maintain Class Ⅰ standard in the future. It is found that the water quality of the reservoir is affected by the water level, flow and exogenous pollution input under the influence of seasonal water level scheduling. This study can help better manage water resources with the approach of high precision water quality information.