LAUSR

As a result of the integration of the stock industry into the entire international economic system, stock companies publish hundreds of prospectuses every second. The ability to quickly and accurately classify the companies and categories to which these data belong, as well as improve the performance of the economic system, has become the key to unlocking its corresponding value at this stage. In order to solve this problem, graph neural network techniques are used to accomplish the classification of stocks in the science and technology version, thus indirectly alleviating the enormous pressure on the economic system. In this study, to complete semi-supervised classification, we propose the PA-GCN model, which takes the lead in graph attention calculation of stock nodes and introduces the Elu activation function. Specifically, in this study, we constructed a stock dataset and implemented a dropout layer to prevent overfitting. The final results on the stock dataset and the publicly accessible Cora dataset demonstrate that this strategy may effectively improve the economic system and that the model has good performance. In the two dataset tests, classification accuracy can be attained at 81.69% and 81.2%, respectively. It also demonstrates the method’s viability for classifying stock nodes.