Devolatilization is always the primary process in biomass thermal conversion, and directional devolatilization has caught considerable attention in recent decades for producing certain fuels and raw chemical materials. In the present study, we report a novel sensitivity study for biomass directional devolatilization using random forest models, which shows obvious advantages in the parameter range, analysis time, and cost compared with the experimental approach. First, a biomass devolatilization product database is constructed with a detailed mechanism for various biomass types under different operation conditions. Then random forest models are developed from the constructed database to accelerate the Sobol sensitivity analysis for obtaining the full-parameter-effect phase diagram. The phase diagram shows that the cellulose fraction holds the maximum influence for the CH4, C2H4, CO, and tar yields, while it has has limited effects on the H2O, CO2, and solid residue (SR) yields. The final temperature has the maximum effect on the H(2 )yield, and the LIG-C fraction shows the dominating effect on the SR yield. The final temperature and the LIG-C fraction have comparable and considerable effects on the H2O yield. This full-parameter phase diagram provides an efficient way to directionally choose the biomass types and alter the operation conditions to produce certain devolatilization products.