Objective：To analyze the seasonal patterns and long-term trends of the 10 year epidemic characteristics of four types of viral hepatitis in China from 2012 to 2021，and explore a time series model suitable for forecasting predicting hepatitis incidence， providing reference and suggestions for scientific hepatitis prevention and control. Methods：Seasonal decomposition of the time series was conducted on the monthly incidence of hepatitis A，B，C，and E in China from January 2012 to December 2021. A seasonal autoregressive integrated moving average model（ARIMA）and a seasonal index smoothing model（ES）were established to predict the incidence of four types of hepatitis from January to August 2022，and the predictive effects were compared. Results：March of each year is the peak period for the incidence of all types of hepatitis. Over the 10 year period，the hepatitis A showed an overall decreasing trend，hepatitis B had fluctuating trends with recent years showing an increasing trend，hepatitis C showed an overall increasing trend， and hepatitis E remained stable overall. The monthly average incidence of hepatitis B，C，and E were 57.06 times，11.5 times，and 1.35 times higher than that of hepatitis A，respectively. The prediction performance of the seasonal ES model was better than that of the seasonal ARIMA model. Conclusion：There are a large number of patients with hepatitis B and C in China，and key prevention and control efforts need to be strengthened. The seasonal decomposition of time series can be used to analyze the seasonal patterns and long -term trends of hepatitis prevalance. The seasonal ES model includes three parameters：level，trend，and seasonality，which can reflect the epidemic pattern of hepatitis. In the prediction of hepatitis incidence，it has the advantages of being simple，easy to calculate，and high prediction accuracy.