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Because most of the scientific literature data is unmarked, it makes semanticrepresentation learning based on unsupervised graph become crucial. At the sametime, in order to enrich the features of scientific literature, a learningmethod of semantic representation of scientific literature based on adaptivefeatures and graph neural network is proposed. By introducing the adaptivefeature method, the features of scientific literature are considered globallyand locally. The graph attention mechanism is used to sum the features ofscientific literature with citation relationship, and give each scientificliterature different feature weights, so as to better express the correlationbetween the features of different scientific literature. In addition, anunsupervised graph neural network semantic representation learning method isproposed. By comparing the mutual information between the positive and negativelocal semantic representation of scientific literature and the global graphsemantic representation in the potential space, the graph neural network cancapture the local and global information, thus improving the learning abilityof the semantic representation of scientific literature. The experimentalresults show that the proposed learning method of semantic representation ofscientific literature based on adaptive feature and graph neural network iscompetitive on the basis of scientific literature classification, and hasachieved good results.

Semantic Representation Learning of Scientific Literature based on Adaptive Feature and Graph Neural Network