Abstract: In the study, the Pinus densata was taken as the research object, based on the measurement data of 113 individual-tree aboveground biomass. By constructing univariate and binary aboveground biomass models of P. densata, six-step method and Taylor series were used to analyze the uncertainty of the model's residual variation and parameter error. The data analysis shows that the uncertainty caused by univariate aboveground biomass model residual variation based on the biomass of 113 P. densata trees was about 6.20%, the parameter uncertainty was 30.30%, the comprehensive uncertainty was about 30.83%; the uncertainty caused by binary aboveground biomass model residual variation was about 5.17%, the parameter uncertainty was 3.12%, the comprehensive uncertainty was about 6.04%. When the modeling sample size increases from 38 to 71, the uncertainty of the model changes significantly. The parameters uncertainty and residual variation uncertainty of the unary model were respectively reduced 14.50% and 1.25%, the comprehensive uncertainty was reduced 14.52%; the parameters uncertainty and residual variation uncertainty of the binary model were reduced 35.65% and 5.80%, the comprehensive uncertainty was reduced 34.96%. With the increase in the number of grouped samples, the uncertainty of the residual variation of the model was gradually reduced. Model residual variation uncertainty and parameter uncertainty were the main sources of uncertainty in the construction of individual tree above-ground biomass models. Increasing model parameters was effectively reduced the uncertainty caused by parameters in individual above-ground biomass models; the increase in the number of samples were reduced the uncertainty of model parameters and the uncertainty of residual variation effectively.