This work aims to study applying the graph neural network (GNN) in cross-border language planning (CBLP). Consequently, following a review of the connotation of GNN, it puts forward the research method for CBLP based on the Internet of Things (IoT)-native data and studies the classification of language texts utilizing different types of GNNs. Firstly, the isomorphic label-embedded graph convolution network (GCN) is proposed. Then, it proposes a scalability-enhanced heterogeneous GCN. Subsequently, the two GCN models are fused, and the research model-heterogeneous InducGCN is proposed. Finally, the model performances are comparatively analyzed. The experimental findings suggest that the classification accuracy of label-embedded GNN is higher than that of other methods, with the highest recognition accuracy of 97.37% on dataset R8. The classification accuracy of the proposed heterogeneous InducGCN fusion model has been improved by 0.09% more than the label-embedded GNN, reaching 97.46%.

中文翻译：

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基于图神经网络的物联网原生数据跨境语言规划探索

本工作旨在研究图神经网络（GNN）在跨境语言规划（CBLP）中的应用。因此，在回顾了GNN的内涵之后，提出了基于物联网（IoT）原生数据的CBLP研究方法，并研究了利用不同类型的GNN对语言文本进行分类。首先，提出了同构标签嵌入图卷积网络（GCN）。然后，它提出了一种可扩展性增强的异构 GCN。随后将两种GCN模型融合，提出了研究模型——heterogeneous InducGCN。最后对模型性能进行了对比分析。实验结果表明，标签嵌入 GNN 的分类准确率高于其他方法，在数据集 R8 上的识别准确率最高，为 97.37%。