This study investigated Pinus massoniana forest in Jiangxi Province. Based on plot investigation and measurement of sample carbon content, the carbon density of Pinus massoniana forest ecosystem was calculated. The influence factors on ecosystem carbon density were screened from fifteen factors, including site, vegetation and meteorological factors by using multiple linear stepwise regression method. The relationship models between ecosystem carbon density and influence factors were established by using ordinary least squares model (OLS), spatial error model (SEM), spatial lag model (SLM) and geographically weighted regression model (GWR), and the best fitting model was selected from them. The results showed that the influence factors of ecosystem carbon density were elevation, slope, soil depth, diameter at breast height (DBH), mean annual temperature and annual precipitation. The fitting results of four models showed that ecosystem carbon density was negatively correlated with slope and positively correlated with elevation, soil depth and DBH. The determination coefficient (R²) of four models were GWR (0.8043) > SEM (0.6371) > SLM (0.6364) > OLS (0.6321). The largest mean square error (MSE) and Akaike information criterion (AIC) of the model was OLS, and the smallest was GWR. The residual tests showed that GWR could effectively reduce the spatial autocorrelation of model residuals. In conclusion, the fitting effect of GWR was the best, which was more suitable for estimating carbon density of Pinus massoniana forest ecosystem in Jiangxi Province.