Abstract: In order to investigate the influence of fiber chemical composition on interfacial properties of the reinforced composites, the back propagation (BP) neural network training sample was constructed with the composition cellulose, hemicellulose, pectin, lignin, water-soluble material, grease waxand moisture regain of bast fibers as factors, and the interfacial properties of bast fibers/unsaturated polyester resin (UP) composites as the results. The gray relational analysis method was employed to investigate the correlation degree of factors which have influence on the interfacial properties of bast fibers/UP composites, and the factors were ranked according to the size of influence degree.A three layer BP neural network model was constructed for iterative training. The effects of chemical composition content on interfacial properties of bast fibers/UP composites can be predicted. The prediction results show that the prediction model output is close to the measured values after learning. It proves that the BP neural network has strong ability to learn which means that the BP neural network can be used in the prediction of interfacial shear force of bast fibers/UP composites. The prediction accuracy is greatly improved and can be reduced by as large as 83.28% with the combination of gray relational analysis and BP neural network.