  The number of scientific papers has increased rapidly in recent years. How to
make good use of scientific papers for research is very important. Through the
high-quality classification of scientific papers, researchers can quickly find
the resource content they need from the massive scientific resources. The
classification of scientific papers will effectively help researchers filter
redundant information, obtain search results quickly and accurately, and
improve the search quality, which is necessary for scientific resource
management. This paper proposed a science-technique paper classification method
based on hypergraph neural network(SPHNN). In the heterogeneous information
network of scientific papers, the repeated high-order subgraphs are modeled as
hyperedges composed of multiple related nodes. Then the whole heterogeneous
information network is transformed into a hypergraph composed of different
hyperedges. The graph convolution operation is carried out on the hypergraph
structure, and the hyperedges self-attention mechanism is introduced to
aggregate different types of nodes in the hypergraph, so that the final node
representation can effectively maintain high-order nearest neighbor
relationships and complex semantic information. Finally, by comparing with
other methods, we proved that the model proposed in this paper has improved its
performance.
