## 4 Experiment
### Results Analysis
Once the relevant fault entities are identified, the appropriate inference results are derived through entity relationship matching within the knowledge graph. The inference results from the knowledge graph are input into the text generation model to produce fault diagnosis text. During the text generation phase, to evaluate the effectiveness of the consumer electronics fault text generation model, the fine-tuned CPT model is trained and compared with other advanced models on the same dataset. The experimental results are shown in Table 5.  
In the consumer electronics fault text generation task, this model is compared with the mT5 model, showing improvements of 8.5 and 7 points in LCSTS (Rouge-L) and CSL (Rouge-L), respectively, and a 0.5-point increase in ADGEN (BLEU-4). Compared to the BART model, LCSTS (Rouge-L) and CSL (Rouge-L) improved by 4.2 and 1.6 points, respectively, and ADGEN (BLEU-4) increased by 0.8 points. When compared with the RoBERTa model, LCSTS (Rouge-L) improved by 1 point. This is because the model in this study improves text generation performance through additional pre-training on natural language generation tasks. These results indicate that our model outperforms others in the consumer electronics fault text generation task. To further demonstrate the superiority of our model, we conducted comparative experiments with state-of-the-art models in the field, using the same dataset across all models. We selected the CLIP model for comparison. CLIP is a model that unifies image and text embeddings into a shared space, using contrastive learning to train image and text representations simultaneously. This makes CLIP a suitable baseline for evaluating multimodal data processing performance. Additionally, we chose GCN and GAT models for comparison, as both are deep learning models designed for handling graph-structured data and are widely applied in tasks such as node classification and edge prediction across various domains, including image, text, and social network analysis. Therefore, GCN and GAT serve as benchmarks for evaluating performance on complex graph structures. We selected accuracy, precision, and F1 score as evaluation metrics. Each model was tested multiple times, and the average of these experimental results was taken as the final outcome. The experimental results are shown in Table 6. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Accuracy (\%)** & **Precision (\%)** & **F1 Score (\%)** \\ \hline GCN & 82.2 & 82.1 & 72.3 \\ GAT & 83.5 & 83.5 & 74.8 \\ CLIP & 88.1 & 86.3 & 78.9 \\ Ours & 92.1 & 89.4 & 80.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of experimental results. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Knowledge Graph Entity Matching** & **Fault Entities** & **The Inference Results from the Knowledge Graph** \\ \hline CF2 CI2 (81\%) & Air Conditioner & From leakage, the internal pipes of the air conditioner are damaged. \\ Hg (69\%) & Smart Bulb & Mercury vapor leakage, immediately cut off power and clean up debris \\ H2S (61\%) & Computer & The computer motherboard burned out \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Inference result. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **LCSTS (Rouge-L)** & **CSL (Rouge-L)** & **ADGEN (BLEU-4)** \\ \hline mT5 & 33.5 & 56.7 & 10.2 \\ BART & 37.8 & 62.1 & 9.9 \\ RoBERTa & 41.0 & - & - \\ Ours & 42.0 & 63.7 & 10.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of text generation model performance. The analysis of the experimental results shows that our model achieved excellent performance across all metrics, indicating that it outperforms other models in the task of consumer electronics fault diagnosis. The performance of CLIP ranks second, attributed to its significant advantage in multimodal feature alignment, allowing it to achieve effective multimodal fusion. However, its performance in handling complex graph structures is inferior to our model.