A Clustering Method of Case-Involved News by Combining Topic Network and Multi-Head Attention Mechanism.Finding the news of same case from the large numbers of case-involved news is an important basis for public opinion analysis.  Taking into account the internal features of news, the information of cases and the contributions provided by different topics, we propose a clustering method of case-involved news, which combines topic network and multi-head attention mechanism.  Using case information and topic information to construct a topic network, then extracting the global features by graph convolution network, thus realizing the combination of case information and topic information. 
Topic Network
A Clustering Method of Case-Involved News by Combining Topic Network and Multi-Head Attention Mechanism.Finding the news of same case from the large numbers of case-involved news is an important basis for public opinion analysis.  Taking into account the internal features of news, the information of cases and the contributions provided by different topics, we propose a clustering method of case-involved news, which combines topic network and multi-head attention mechanism.  At the same time, the local features are extracted by multi-head attention mechanism. 
Multi-Head Attention Mechanism
Using case information and topic information to construct a topic network, then extracting the global features by graph convolution network, thus realizing the combination of case information and topic information. 
Graph Convolution Network
Finally, the fusion of global features and local features is realized by variational auto-encoder, and the learned latent representations are used for clustering. 
Variational Auto-Encoder