The Prediction of Tensile Strength Performance of Fiber‐Reinforced Composites Based on a Knowledge Graph Constructed From Material Literature

ABSTRACT Understanding the relationship among material composition, processing methods, and mechanical properties plays an important role in the prediction of the tensile strength properties of polymer composites. Due to polymer composites' long and time‐consuming experimental cycles, obtaining data directly through experiments is challenging. At the same time, with the growing body of related literature, using literature mining and predictive methods to explore the relationships between material composition, processing methods, and mechanical properties has become a feasible alternative. Based on this, we propose an innovative approach that integrates data extraction, knowledge graph construction, and machine learning‐based performance prediction. A comprehensive dataset named ComMat for composites was constructed through systematic data sorting and filtering, encompassing nine entity categories and nine relationship categories representing key parameters such as materials, processing methods, testing, and performance. We proposed a literature data collection method that extracts structured triples from the literature using the joint extraction model PFPMHN, which was then used to build a domain‐specific knowledge graph. This knowledge graph enables the reverse inference of material performance and also supports feature selection through extensive reverse analysis, identifying key features related to material composition, processing parameters, and formulation. These features were used to train machine learning models, including XGBoost, achieving an R 2 value of 0.96 for tensile strength prediction. Additionally, feature importance analysis, SHAP experiments, and OAT sensitivity analysis were conducted to identify further critical features affecting tensile strength. This study significantly improves the accuracy of polymer composite performance prediction. It advances the recognition and expression of graph features, providing actionable insights for optimizing material design and enhancing mechanical performance.