Objective: To analyze the seasonal patterns and long-term trends of the ten year epidemic characteristics of four types of viral hepatitis in China from 2012 to 2021, explore a time series model suitable for predicting hepatitis incidence, and provide reference and suggestions for scientific prevention and control of hepatitis. Method: 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 were established to predict the incidence of four types of hepatitis from January to August 2022, and the predictive effects were compared. Result: March of each year is the peak period for various types of hepatitis, and during the ten-year period, the overall trend of hepatitis A remains decreasing; the overall trend of hepatitis B has been increasing and decreasing, with an upward trend in recent years; the overall trend of hepatitis C remains increasing; the overall trend of hepatitis E remains stable. The average monthly incidence of hepatitis B, C, and E is 57.06 times, 11.5 times, and 1.35 times higher than that of hepatitis A, respectively. The prediction performance of the seasonal index smoothing model is 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 measures should be strengthened. The seasonal decomposition of time series can be used to analyze the seasonal patterns and long-term trends of hepatitis epidemic characteristics. The seasonal index smoothing model includes three parameters: level, trend, and season, which can reflect the epidemic pattern of hepatitis incidence, in the prediction of hepatitis incidence, it has the advantages of simple model, simple calculation, and high prediction accuracy.