## 4 Results and Discussion
### Experiment IV: The Performance of Classifier Trees Based on Various Models
Various indicators of the test results are presented in Table 6. These metrics are divided into micro- and macro-average types. The former measures the classification performance of a classifier tree. By contrast, the latter measures the average classification performance of all classifiers on each label in the classifier tree. The results in Table 6 indicate that the proposed model \"XLNet + BiLSTM + HA + FC + TL\" achieved the best classification performance for all metrics. The conclusions from the first three experiments are obtained and verified. Clearly, (1) XLNet performs significantly better than other pretrained models in extracting semantic embeddings, and (2) BiLSTM + HA has significantly improved performance compared to HA. At the same time, (3) models using TL can slightly improve the classification performance on their original basis, and this degree of improvement weakens the overall performance improvement. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Micro**} & \multicolumn{3}{c}{**Macro Avg.**} \\ \cline{2-7}  & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline XLNet + BiLSTM + HA + FC + TL & 0.961 & 0.961 & 0.961 & 0.946 & 0.920 & 0.929 \\ XLNet + BiLSTM + HA + FC & 0.957 & 0.957 & 0.957 & 0.939 & 0.914 & 0.919 \\ XLNet + HA + FC + TL & 0.882 & 0.882 & 0.882 & 0.778 & 0.836 & 0.789 \\ XLNet + HA + FC & 0.851 & 0.851 & 0.851 & 0.788 & 0.817 & 0.766 \\ WWM + HA + FC & 0.857 & 0.857 & 0.857 & 0.737 & 0.693 & 0.692 \\ BERT + HA + FC & 0.825 & 0.825 & 0.825 & 0.741 & 0.727 & 0.705 \\ ERNIE3.0 + HA + FC & 0.673 & 0.673 & 0.673 & 0.535 & 0.491 & 0.467 \\ XLNet + FT + FC & 0.580 & 0.580 & 0.580 & 0.392 & 0.346 & 0.340 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The performance of the classifier tree based on various models. ## 5 Conclusions

To solve the hierarchical multi-label classification problem of PGTs, this study proposes an HTMC-PGT framework. The most prominent feature of this framework is transforming the traditional HMTC problem into a parameter solving problem for various multi-class classifiers in a classifier tree. In this way, the HTMC-PGT framework brings more flexibility to problem solving. In particular, (1) gradually training classifiers based on the classifier tree structure can effectively solve the problems of sample scarcity and imbalance in leaf node labels on hierarchical label trees. In addition, to improve the overall performance of the classifier tree, relevant experiments are conducted on the pretrained extraction model of character semantic embedding, refining the model of document-level semantic embedding and transfer learning between the parent and child classifiers in the classifier tree. It is found that (2) XLNet performs better than other pretrained models such as BERT in HTMC-PGT; (3) BiLSTM + HA can extract document-level embedding vectors that are more conducive to text classification; and (4) on the whole classifier tree scale, the transfer learning (TL) between parent and child node classifiers can increase the overall classification performance of the classifier tree to a certain extent. The above conclusions obtained during the training phase of independent learning of each classifier are equally valid during the testing phase of the classifier tree as a whole experiment. Moreover, the micro-precision, recall, and F1 values of XLNet + BiLSTM + HA + FC + (TL) proposed in this paper can reach values over 95%, 7.5%, and 38.1% higher than the other baseline models used in this study. The method proposed in this study has some shortcomings. First, the time required for extracting char-level semantic embeddings using XLNet is much longer than that required for models such as BERT. However, by optimizing the program code, the experimental results (1 s per example) in the final testing stage can also satisfy practical requirements. Second, the proposed transfer learning between classifiers requires a semantic parent-child inclusion relationship between classifiers.