## 4 Experimental Results
### Experimental Results  
We evaluated the performance of the proposed hierarchical prototype neural network by comparing it with general deep learning models, including TextRCNN [5], BERT [6], and SGM [32], as well as the top-performing model from the CAIL 2021 competition Phase-1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & **Label Total** & \multirow{2}{*}{**Label Depth**} & **Label** & \multirow{2}{*}{**Training/Validation/Test**} \\  & **Quantity** & & & **Quantity** & \\ \hline CAIL2021 & 320 & 3 & 11/75/234 & 1996/250/250 \\ WOS & 141 & 2 & 7/134 & 30,070/7518/9397 \\ AAPD & 61 & 2 & 9/52 & 5380/1000/1000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset statistics. Figure 3: Distribution of samples with different labels in the dataset. Since Micro-F1 was the primary evaluation metric used in the competition, the experimental results for the CAIL2021 dataset are summarized in Table 2.  
To demonstrate the effectiveness of the proposed method, we conducted a rigorous evaluation of the statistical significance of the performance improvement. Specifically, the CAIL2021 dataset was randomly partitioned into training, validation, and test sets in an 8:1:1 ratio. Subsequently, five independent random experiments were carried out, and the results are summarized in Table 3. A paired samples \(t\)-test was employed to perform significance analysis. Using the experimental data, we evaluated the significance of differences between the proposed method and the micro-f1 means of TextRNN, BERT, and SGM. The detailed significance analysis is presented in Table 4. As shown in Table 4, the performance improvement of the proposed method is statistically significant (\(p<0.05\)) compared to other attention-based models. These evaluation results indicate that our method exhibits substantial advantages over alternative approaches. The experimental results demonstrate that the proposed method significantly enhances multi-label recognition performance. Compared with the top-performing model in the CAIL 2021 competition, our approach achieves a 6.18% improvement in the micro-F1 score, substantially outperforming commonly used deep learning models for this task. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **TextRCNN** & **Ours** & **BERT** & **Ours** & **SGM** & **Ours** \\ \hline \(\mu\)(\%) & 49.21 & 61.06 & 53.22 & 61.06 & 54.21 & 61.06 \\ \(\sigma\) & 0.028 & 0.095 & 0.244 & 0.095 & 0.199 & 0.095 \\ \hline \(n\) & 5 & & 5 & 5 & \\ \(\rho\) & 0.284 & & 0.671 & & 0.579 \\ \(\Delta\) & 0 & & 0 & 0 & \\ \(df\) & 4 & & 4 & 4 & \\ \(tStat\) & \(-86.78\) & \(-47.72\) & \(-41.78\) \\ \(P(T<t)\) & \(5.29\times 10^{-8}\) & \(5.77\times 10^{-7}\) & \(9.81\times 10^{-7}\) \\ \(t\) & 2.132 & & 2.132 & 2.132 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Significance analysis of the performance improvement of the proposed model compared to other models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy** & **Recall Rate** & **Micro-F1** \\ \hline TextRCNN & 73.32 & 37.18 & 49.34 \\ BERT & 72.72 & 42.62 & 53.74 \\ SGM & 63.29 & 48.22 & 54.74 \\ CAIL 2021-Top1 & 57.40 & 53.00 & 55.10 \\ Method in this study & 67.57 & 56.06 & 61.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of CAIL2021 dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & \multicolumn{4}{c}{**Micro-F1**} \\ \hline TextRCNN & 49.34 & 49.28 & 49.31 & 48.93 & 49.19 \\ \hline BERT & 53.74 & 52.88 & 53.66 & 53.23 & 52.59 \\ \hline SGM & 54.74 & 53.95 & 54.02 & 54.61 & 53.71 \\ \hline Ours & 61.28 & 61.27 & 61.13 & 61.07 & 60.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of Models on CAIL2021 datasets. This suggests that our method effectively captures core semantic information within hierarchical levels while better integrating cross-layer semantic relationships, ultimately yielding superior results.