The transaction flow data of campus cards is studied to mine the interesting information to provide scientific basis for decision management. According to the consumption data of all canteens from January 2014 to February 2019, the Holt-Winters multiplication model and the ARIMA model are built with noise canceled by smoothing. The discrete time series composed of monthly data during this period is fitted and analyzed; the consumption trends in March to May 2019 are forecasted, the results of which are tested by actual values. The experiment shows that the Holt-Winters model can well fit the consumption data at higher forecast accuracy. Our method is helpful to master the consumption behavior of teachers and students in the canteens and lays the groundwork for logistics departments to optimize resource allocation and scientific decision-making.