The authors present the Organic Polymer Energy Conversion Materials (OPECM) Dataset, comprising 3,225 polymers systematically categorized into three key areas: organic semiconductors, photovoltaics, and dielectric materials. Leveraging this dataset, they developed polymer unit-guided regression models to accurately predict essential properties: electron/hole mobility, power conversion efficiency, and dielectric constant, while identifying pivotal polymer units that govern material performance. Moreover, using the SISSO method, the authors constructed interpretable symbolic regression models that uncover critical molecular features and their functional roles in energy conversion. This study provides both data and insights to accelerate the rational design of multi-functional energy materials.