News text classification method and system based on deep learning

Through deep learning methods, semantic features are extracted using word vector representation and long and short-term memory networks, combined with hollow convolution and pooling processing, the problem of low accuracy in news text classification in traditional methods is solved, and more efficient text feature extraction and Classification results.

CN119128162AInactive Publication Date: 2024-12-13GUANGDONG OCEAN UNIVERSITY
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Patent Information

Application Number
CN202411609856.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-12
Publication Date
2024-12-13
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses a deep learning-based news text classification method and system, and the method comprises the steps: carrying out the word vector representation of an obtained to-be-classified news text, and obtaining an input matrix corresponding to the to-be-classified news text; semantic features of the input matrix are extracted based on a long short-term memory network, and a text semantic relation corresponding to the to-be-classified news text is obtained; performing convolution processing on the text semantic relation based on hole convolution of different sizes to obtain multi-scale text features corresponding to the news text to be classified; pooling the multi-scale text features, and classifying the pooled multi-scale text features to obtain a news text classification result. The technical problem that in the prior art, news text classification is low in accuracy is solved.
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Citation Information

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