Forest ecosystem is an important part of terrestrial carbon cycle, and its carbon sequestration capacity is significantly higher than other terrestrial ecosystems. Studying forest ecosystem carbon fluxes is the key to understand global carbon cycle. Carbon cycle model is an effective tool to study forest ecosystem carbon fluxes. In this paper, four typical forest ecosystems in China, including the temperate broad-leaved Korean pine forest in Changbai Mountain, the subtropical evergreen coniferous forest in Qianyanzhou, the subtropical evergreen broad-leaved forest in Dinghushan, and the tropical rainforest in Xishuangbanna, are the research objects, using Eddy Covariance technique (EC) observation data to evaluate the effects of the FORRCCHN model on the ecosystem total respiration (ER), gross primary productivity (GPP), and net ecosystem productivity (NEP) of different forest ecosystems. (1) The FORCCHN model can better simulate the carbon fluxes of the four typical forest ecosystems at different time scales. The simulation results of the daily changes of ER and GPP in deciduous broad-leaved forest and evergreen coniferous forest are the best (the correlation coefficients of ER are 0.94 and 0.92, respectively, and the correlation coefficients of GPP are 0.86 and 0.74, respectively). (2) The seasonal dynamic simulation find that the simulated and observed carbon fluxes of the four different forest ecosystems are significantly correlated (P<0.01), the R² value of the ER GPP, NEP observation and simulation are 0.77-0.93, 0.54-0.88, 0.15-0.38, respectively. The model can well simulate the changing laws of carbon sources (NEP>0) and carbon sinks (NEP<0) of the forest ecosystem in different seasons. (3) In the simulation of inter-annual changes, there is a good agreement between the simulated value and the observed value of the inter-annual variation trend line. The model overestimates the ER and GPP of evergreen broad-leaved forest, while it slightly underestimates the ER and GPP of the other three forest ecosystems.