In this paper, we put forth a multi-modal cascade model with feature transfer with the aim of adjusting the characteristics of polymer property prediction. Polymers are characterised by a composite of data in several different formats, including molecular descriptors and additive information as well as chemical structures. Our model enables more accurate prediction of physical properties for polymers by combining features extracted from the chemical structure by GCN with features such as molecular descriptors and additive information. The predictive performance of the proposed method is empirically evaluated using several polymer datasets.We report that the proposed method shows high predictive performance compared to the baseline conventional approach using a single feature.