Forest biomass directly affects the value of forest ecosystem services. Determining how to apply new technologies of landsenses ecology to accurately predict the spatiotemporal evolution of forest biomass at the regional scale is a key strategic issue for the formulation of major national policies and ecological industry systems. The purpose of this study was to construct a set of ecological information diagnosis frameworks to optimize the structure of the 3PG2 forest growth model and to solve the problem of uncertainty in an ecological prediction the defects in the model structure during the process of forest landscape construction. Nanjing County, Fujian Province was selected as the study area, where the Chinese fir forests are widely distributed. A threshold scale was selected, and spatially statistical analysis was used to identify the uncertainty of the biomass simulation results. The Geogdetector software was used to construct an ecological mechanism of multi-factor interactions on the model simulation, and genetic technology was used to optimize the model structure to improve the simulation accuracy. The computer program (python) and the 3PG2 model were used to accurately predict the spatiotemporal evolution trend of the Chinese fir forest biomass on a regional scale. The results showed that forest age is the dominant factor that leads to uncertainty in the biomass simulation results of the 3PG2 model. We conclude that sustainable forest management and ecological information at the regional scale requires the accurate prediction of forest biomass, and can be achieved through the fusion of mix-marching data and optimizing models.