## 4 Conclusions  
Compared with the use of pre-trained language models, our method is indeed not good enough, but our method can solve the shortcomings of the limited text input length of pre-trained language models. We believe that the pre-trained language model is limited by the length of the text, and there is no good solution for text classification with a length greater than 512. Normally, the text is divided into several texts with a length of 512 before processing. Even if the current new pre-trained language models such as XL-Net [21] break through the limitation of text length, it is achieved by increasing the computing performance and increasing the space occupied by the video memory. The longer the text length, the greater the difficulty of pre-trained and the difficulty increases in proportion to the square. When processing this part of the data in the experiment, we only use the first \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Accuracy of Classification** & **Precision** & **Recall** & **F1-Measure** \\ \hline SortPool & 72.22\% & 75.09\% & 73.63\% \\ \hline DiffPool & 77.21\% & 75.56\% & 76.37\% \\ \hline gPool & 74.65\% & 70.31\% & 72.42\% \\ \hline SAGPool & 80.51\% & 78.24\% & 79.36\% \\ \hline Graph U-Nets & 78.16\% & 81.11\% & 79.61\% \\ \hline
**Attention Graph U-Nets (gensim)** & **78.60\%** & **80.55\%** & **79.56\%** \\ \hline
**Attention Graph U-Nets (Bert)** & **81.43\%** & **80.24\%** & **80.83\%** \\ \hline BERT-base (Chinese) & 91.53\% & 89.00\% & 90.25\% \\ \hline BERT-large (Chinese) & 91.44\% & 91.59\% & 91.51\% \\ \hline BERT-wwm-ext (Chinese) & 92.07\% & 93.80\% & 92.93\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of learning experiments in terms of node classification accuracies on our dataset. Attention graph U-Nets denotes our proposed model. 512 words as input to the BERT model, and the other parts can only be discarded, because in Chinese news, the first half of the news likely describes an event, and the follow-up describes the solution or plan of the event. The trend of the description of the event and the solution is the opposite, so the input is divided into sections. The method of the BERT model is not suitable. However, we propose to use graphs to solve this problem to convert texts whose length exceeds the limit into knowledge graphs, extract high-dimensional features and use them to achieve classification, without being restricted by length. In other words, the longer the text length, the more useful triples can be extracted from the knowledge graph we construct, and the relationships between these triples can be used to classify them. Of course, this also leads to the second problem. Knowledge graphs do lose part of the information during the conversion process. Compared with text, knowledge graphs can only extract the main structure of sentences and construct triples. For some attributive adverbials and other information, the loss is still serious. Our solution to this is as mentioned in Section 2.2. In the knowledge graph, we make up five virtual nodes, which are keywords, high-frequency words, news characters, news locations, and news organizations, and set the initial vector of these nodes to a zero vector to increase the connectivity and relevance in the knowledge graph. We found that relatively short news text (for example, the text length is less than 100) does not work well for classification. The reason is that the text length is too short, which is not conducive to us extracting enough entities to build triples and knowledge graphs. In summary, this paper has made two contributions. (1) We construct a Chinese news data set predicted by more than 9000 annotated news time trends. (2) We design an improved method based on a pre-trained language model and graph pooling algorithm, which can better balance accuracy and processing long-text news. Conceptualization, X.H. and Z.C. ; software, X.H. ; validation, J.P.; formal analysis, X.X. ; resources, R.C. ; data curation, T.P. ; writing--original draft preparation, X.H.