China boasts the world’s largest plantation forest of fast-growing poplar trees. However, the wood from these plantations typically exhibits high moisture content, leading to issues such as cracking and warping upon drying. The primary objective of this study was to evaluate the statistical suitability of Weibull, normal, and log-normal distributions for modeling the modulus of elasticity (MOE) of timber and to classify the strength of fast-growing poplar wood based on its strength characteristics to facilitate standardized utilization. Visual grading was employed to remove wood having apparent defects, thus mitigating the influence of wood defects and drying processes on strength. Subsequently, machine grading was conducted using static bending tests to assess the applicability of normal, log-normal, and Weibull distributions to the modulus of elasticity (MOE) distribution. Additionally, the study utilized a correlation matrix to explore the impact of density and moisture content on MOE. The findings suggest that both normal and Weibull distributions are suitable for characterizing the MOE of Chinese fast-growing poplar wood, while the log-normal distribution is not. The mean characteristic values of the MOE and density were 12.21 GPa and 521 kg/m³, respectively. The sampled poplar wood was categorized as C30 grade. Both density and moisture content were found to exert significant influences on the MOE (p < 0.01). However, density alone is not a reliable predictor for estimating MOE (R² = 0.511).