<p>Advancing the integration of artificial intelligence and polymer science requires high-quality, open-source, and large-scale datasets. However, existing polymer databases often suffer from data sparsity, lack of polymer-property labels, and limited accessibility, hindering systematic modeling across property prediction tasks. Here, we present OpenPoly, a curated experimental polymer database derived from extensive literature mining and manual validation, comprising 3985 unique polymer-property data points spanning 26 key properties. We further develop a multi-task benchmarking framework that evaluates property prediction using four encoding methods and eight representative models. Our results highlight that the optimized degree-of-polymerization encoding coupled with Morgan fingerprints achieves an optimal trade-off between computational cost and accuracy. In data-scarce condition, XGBoost outperforms deep learning models on key properties such as dielectric constant, glass transition temperature, melting point, and mechanical strength, achieving R2 scores of 0.65—0.87. To further showcase the practical utility of the database, we propose potential polymers for two energy-relevant applications: high temperature polymer dielectrics and fuel cell membranes. By offering a consistent and accessible benchmark and database, OpenPoly paves the way for more accurate polymer-property modeling and fosters data-driven advances in polymer genome engineering.</p>