In this study, we developed a sophisticated approach that combines DFT calculations and datadriven ML models to accurately predict the E exp gap values of CPs. We demonstrated that by modifying oligomer structures-specifically, removing alkyl side chains and extending conjugated backboneswe can effectively capture the electronic properties of CPs. This modification significantly improved the correlation between E DF T gap and E exp gap , achieving an R 2 value of 0.51, while also considerably reducing computational time consumption. In contrast, unmodified monomer structures yielded a notably low R 2 value of 0.15. To further enhance the prediction accuracy of E exp gap , we trained a variety of ML models using both E DF T gap and conventional molecular representations as inputs. Compared to the baseline model trained with only molecular representations, incorporating E DF T gap of modified oligomers not only improves prediction accuracy-reflected by an R 2 of 0.77 and a mean absolute error (MAE) of 0.065 eV achieved by XGBoost-but also enhances the models' transferability in predicting the optical gaps of new polymers outside the design space of the training dataset. Our work outlines a rational strategy for predicting fundamental properties of polymers by segmenting them into different substructures. These substructures are then characterized using different levels of theoretical or methodological approaches based on how well they correlate with the target properties.