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Interconnection of all things challenges the traditional communication methods, and Semantic Communication and Computing (SCC) will become new solutions. It is a challenging task to accurately detect, extract, and represent semantic information in the research of SCC-based networks. In previous research, researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node classification. However, the content of semantic information is quite complex. Although graph convolutional neural networks provide an effective solution for node classification tasks, due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures, the extracted feature information is subject to varying degrees of loss. Therefore, this paper extends from a single-layer topology network to a multi-layer heterogeneous …