## 4 Experiment
### Ablation Experiment Results and Analysis  
Ablation experiments can be used to demonstrate the effectiveness of each module in the relation classification model proposed in this study. The experimental method was to add modules to the baseline model for the experiments. The ablation experiments were divided into three groups, namely the baseline model + cascade attention module (base + cascade attention), the baseline model + feature splicing module (base + concat), and the extreme model + cascade attention module + feature splicing module (base + cascade attention + concat). The experiments were carried out on the three datasets on the three groups of models, and the F1-value obtained by the model was used to evaluate the performance of the model. The experimental results are shown in Table 4.  
The data with higher accuracy in the same category has been highlighted in bold in the table.From the data in Table 4, it can be seen that the two modules proposed in this paper were effective. It can be seen from the data that there were three indicators for each dataset, namely precision (P), recall (R), and F1. Only the maximum value of the P index on the KBP37 dataset appeared for the \"base + concat\" model. The maximum values of all other indicators appeared for the \"base + cascade attention + concat\" model, which showed that the improvement effect of the two modules on the model was relatively obvious. The  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Standard Text Dataset**} & \multicolumn{3}{c}{**SemEval-2010 Task 8**} & \multicolumn{3}{c}{**KBP37**} \\ \cline{2-10}  & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline Base & 77.59\% & 78.22\% & 77.90\% & 88.03\% & 90.12\% & 89.06\% & 68.38\% & 69.47\% & 68.92\% \\ Base + concat & 78.15\% & 78.67\% & 78.40\% & 88.93\% & 90.02\% & 89.47\% & **69.76\%** & 69.89\% & 69.82\% \\ Base + cascade attention & 78.11\% & 78.40\% & 78.24\% & 88.70\% & 89.77\% & 89.23\% & 69.34\% & 70.11\% & 69.72\% \\ Base + cascade attention + concat & **78.33\%** & **79.32\%** & **78.80\%** & **89.19\%** & **90.32\%** & **89.75\%** & 69.75\% & **70.82\%** & **70.28\%** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experimental results on three datasets.  
\"base + concat\" model and the \"base + cascade attention\" model added a cascade attention module and an attention module, respectively. By comparing the data for the \"base\" model, only the R indicator in the SemEval-2010 Task 8 dataset and the P indicator in the KBP37 dataset declined, and other indicators improved to varying degrees, which can explain that a single module is also effective at improving the effect of the model.  
It can be seen from the data that the cascaded word vector attention module and feature splicing module on the three datasets both promoted the relationship classification task. The cascaded word vector attention module amplified the promotion effect of positive features on model performance multiple times by performing two feature enhancements in the word vector dimension and promoted the development of word vectors to be more suitable for relational classification tasks. The feature splicing module fused the context information at the sentence level and the word information at the entity level. The fusion feature not only contained the relationship features between the two entities, but also contained all the information of the sentence where the two entities were located. The model can learn features from two entities, and the model can also learn the relationship between two entities at the sentence level. The feature learning of the two aspects can promote the improvement of the classification effect of the model.