## 4 Experiment
### Results Analysis  
To validate the performance of the proposed multimodal GraphSAGE model for consumer electronics fault diagnosis, we compared it with other advanced models on the same dataset. The experimental results are presented in Table 3, where '-SI' represents the results using single-modality image features, and '-M' represents the results using multimodal features. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **mAP (\%)** & **F1 (\%)** & **P (\%)** \\ \hline GNN-SI & 44.2 & 45.2 & 51.1 \\ GCN-SI & 51.7 & 48.1 & 54.5 \\ GAT-SI & 57.5 & 49.9 & 57.9 \\ GraphSAGE- SI & 56.2 & 51.8 & 60.1 \\ GraphSAGE- M & 59.8 & 50.2 & 62.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of models. As shown in Table 3, when using single-modal information alone, we observe that GAT achieves a higher mAP score than GraphSAGE. However, after incorporating multimodal information, GraphSAGE surpasses GAT in terms of mAP. This might be due to the fact that under single-modal conditions, the structural complexity of GraphSAGE, which is an advantage in handling complex graph relationships, might not capture information as comprehensively as GAT when processing single-modal data. In contrast, when multiple modalities are integrated, GraphSAGE demonstrates superior performance over other comparison models. This improvement could be attributed to its adaptability and scalability, allowing it to leverage shared graph structures effectively and capture the relational information across different modalities as node information increases. In terms of F1 score, the GraphSAGE model shows better performance under single-modal conditions, which may be due to the model's recall performance influencing the result. The precision trends align with the mAP results, showing improved performance with multimodal information. This consistency across metrics highlights the model's capability to leverage multimodal data for more accurate fault diagnosis in consumer electronics. The combination of knowledge graphs with graph neural networks enables the fault detection model for consumer electronics to achieve improved performance and enhanced fault description capabilities. Compared to traditional fault detection methods or single-modal deep learning-based approaches, this model incorporates richer prior knowledge, transforming the previous black-box model into a more interpretable framework. This enhanced interpretability and knowledge integration allow the model to be more effectively applied in practical scenarios. To validate the performance of the model designed in this study, we set up a smart home scenario for consumer electronics. In this scenario, when a consumer electronic device malfunctions, such as a rupture within an air conditioner's internal piping, users may find it challenging to detect the issue. At this point, the user uploads fault information (e.g., fault description, data collected from sensors). The model then extracts multimodal features through the feature extraction network, which are input into the GraphSAGE graph neural network for reasoning. The inference reveals an excessive level of CF\({}_{2}\)Cl\({}_{2}\), which is then matched with fault nodes in the knowledge graph. Using a semantic matching template, the model identifies the fault entity (air conditioner) and provides a diagnosis result (Freon leakage, the internal pipes of the air conditioner are damaged), as illustrated in Figure 6.  
Using GraphSAGE to process multimodal data enhances the model's understanding and handling of fault data, ensuring that the resulting fault diagnosis outcomes are interpretable. Table 4 shows the inference results of the multimodal knowledge graph in consumer electronics and home scenarios. The fault results obtained through GraphSAGE are matched with knowledge graph entities to identify the head entity. Subsequently, the knowledge graph inference retrieves corresponding fault entities such as \"air conditioner\", \"smart bulb\", and  
Figure 6: Knowledge graph reasoning. \"computer\".