Software change prediction, aimed at identifying change-prone modules, can help software managers and developers allocate resources efficiently and reduce maintenance overhead. Extracting effective features from the code plays a vital role in the construction of accurate prediction models. In recent years, researchers have shifted from traditional hand-crafted features to semantic features with powerful representation capabilities for prediction. They extracted semantic features from abstract syntax tree (AST) node sequences to build models. However, existing studies have ignored the structural information in the AST and the rich semantic information in the code. How to extract the semantic features of the code is still a challenging problem. For this reason, the study proposes a change prediction method based on hybrid graph representation. To start with, the model combines AST, control flow graph (CFG), data flow graph (DFG), and other structural information to construct the program graph representation of the code. Then, it uses the graph neural network to learn the semantic features of the program graph and the features obtained to predict change-proneness. The model can integrate various semantic information to represent the code better. The effectiveness of the proposed method is verified by comparing it with the latest change prediction methods on various change datasets.