Abstract: The method of predicting the properties of compounds based on machine learning plays an important role in the virtual screening of materials discovery. Existing methods of traditional machine learning models have to manually extract features, which have problems such as difficulty in extract features and difficulty in processing Simplified Molecular Input Line Entry System（SMILES） codes. In order to solve these problems, this paper proposes an end-to-end graph neural network to predict the high temperature performance of resin materials. First, the SMILES code of the resin material is represented as a graph, where the vertices are atoms and the edges are chemical bonds. Then, the vector representation of the molecule is obtained by constructing the graph neural network of the molecular graph. Finally, a full connection neural network contains information such as molecular vector, environment, and conditions to predict the maximum temperature by regression model. Experiments of the resin material data set show that the end-to-end graph neural network model proposed in this paper has more than doubled the accuracy of model prediction compared with the traditional machine learning models.