The prediction of fragrance retention grades (persistence) of monomer flavors is of great importance for the development of perfumery technology. Traditional methods are susceptible to subjective judgments and complex model decisions. Therefore, the paper proposes to investigate the three-dimensional dimension of monomer flavors and construct a monomer flavors retention classification framework based on a three-dimensional graph convolutional network (3DGCN) model. The 3DGCN model shows better performance in the task of predicting the fragrance retention grades compared to 2D research methods, especially when using set2set pooling in the graph aggregation stage, the classification results of the 3DGCN model with 82.06% accuracy, 82.34% precision, 81.98% recall, and 82.18% F1 score. This experiment is based on a three-dimensional graphical convolutional network framework for monomer flavors retention classification (MFRC-3DGCN), which provides a new dimension of data consideration for the prediction of fragrance retention grades and a reliable tool for the evaluation of fragrance retention grades properties of monomer flavors.