Forest resource inventory is an important scientific basis for comprehensively understanding the effectiveness of China's forestry ecological construction and formulating forestry sustainable development strategies. However, due to updates in forestry monitoring systems and changing requirements for ecological environment construction over time, there are some missing factors existing in forest resource inventory. This study is based on the data from the sixth (1999-2003) to ninth (2014-2018) periods of forest resource continuous inventory in East China. First, we selected feature factors based on the results of correlation analysis of five forest stand structure factors including vegetation type, tree species composition, forest community structure, regeneration level, and naturalness. Then, we used random forest classification models to fill in the missing factors and assessed the accuracies of models, as well as examined the external validity of models. Finally, we analyzed the importance of feature factors. Our results show that: (1) the correlation coefficients between missing factors and factors from the same period are overall higher than those with factors from later periods. Among them, the average correlation coefficients between vegetation type and tree species composition and other related factors are 0.868 and 0.733, significantly higher than the other three missing factors; (2) overall, the random forest classification models achieve an accuracy of 0.770 or higher in all five missing factors, demonstrating outstanding external validity at both provincial and county scales. Moreover, models corresponding to factors with high correlation coefficients exhibit relatively higher accuracy (the accuracies of vegetation type and tree species composition are 0.900 and 0.841, respectively); and (3) the results of feature factor importance align closely with the findings of the correlation analysis, indicating that a higher proportion of the same-period factors within the feature factor combination contributes to enhancing the model imputation performance. Additionally, the missing factor itself exhibits a significant contribution to improving the model imputation performance in relation to the subsequent-period values. The findings of the study can be utilized to refine the dynamic monitoring database of forest resources, thereby providing support for a scientific evaluation of the effectiveness of China's forestry protection programs, and the enhancement of China's forest classification and management system. In future research, based on the fundamental strategy of promoting the high-quality development of national forests, rigorous experimental designs should be employed to evaluate the effectiveness of China's forest classification and management systems. This effort is crucial for refining our country's compensation mechanism for forestry ecological construction and establishing a stable, healthy, high-quality, and efficient forest ecosystem.