A code context model comprises source code elements and their relations relevant to a programming task. The capture and use of code context models in software tools can benefit software development practices, such as code navigation and search. Prior research has explored approaches that leverage either the structural information of code or interaction histories of developers with integrated development environments to automate the construction of code context models. However, these approaches primarily capture shallow syntactic and lexical features of code elements, with limited ability to capture contextual and structural dependencies among neighboring code elements. In this paper, we propose GNNContext, a novel approach for predicting code context models based on Graph Neural Networks. Our approach leverages code representation learning models to capture both the syntactic and semantic features of code elements, while employing Graph Neural Networks to learn the structural and contextual information among neighboring code elements in the code context models. To evaluate the effectiveness of our approach, we apply it to a dataset comprising 3,879 code context models that we derive from three Eclipse open-source projects. The evaluation results demonstrate that our proposed approach GNNContext can significantly outperform the state-of-the-art baseline for code context prediction, achieving average improvements of 62.79%, 56.60%, 73.50% and 81.89% in mean reciprocal rank, top-1, top-3, and top-5 recall rates, respectively, across predictions of varying steps. Moreover, our approach demonstrates robust performance in a cross-project evaluation setting. Our code is publicly available at https://github.com/ZXXYy/CodeContextModel.