Scientific and technological paper classification method based on stacked automatic encoder and citation network

Abstract

A science and technology paper classification method based on a stacked automatic encoder and a citation network comprises the following steps: building a citation network adjacency matrix and a labelvector matrix according to existing science and technology paper data, and calculating a probability transition matrix and a co-occurrence probability matrix; constructing a science and technology paper classification model based on the stacked automatic encoder and a long-term and short-term memory network; training the stacked automatic encoder and the long-term short-term memory network by using the co-occurrence probability matrix and the label vector matrix; and using the trained model to predict the classification result of the new paper. According to the method, the reference relationship among the papers is considered, the internal relationship among the nodes in the constructed reference network is effectively extracted and mapped into the low-dimensional embedded vector space, and the category feature information is acquired by using the long-term and short-term memory network, so that the category of the new papers is accurately predicted.

Description

technical field [0001] The invention relates to the fields of network science and machine learning, in particular to a method for classifying scientific papers based on a stacked autoencoder and a citation network. Background technique [0002] With the development of society, the quality and quantity of academic papers play an irreplaceable role in the cultivation of talents and the allocation of resources. Scientific researchers often refer to the articles and achievements published by predecessors in this field to achieve breakthroughs and improvements when doing research in a specific field. In the papers to be published, references are a major focus, explaining the basis and origin of the work done in the paper. Therefore, the citation relationship between papers constitutes a huge and complex citation network. As time goes by, the scale of the citation network will become larger and larger, and as a result, the management of papers will become more and more difficult...

AI Technical Summary

Problems solved by technology

[0004] However, the above sampling methods can only obtain a part of the node sequence

Moreover, shallow models cannot capture nonlinear citation network information

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