Title: A cultural industry text classification method based on knowledge graph information constraints and knowledge fusion

Authors: Xue Ji

Addresses: School of Cultural Industries Management, Communication University of China, Beijing, 100024, China

Abstract: The study proposes a text classification method for the cultural industry. It uses knowledge graph information constraints and fusion. A knowledge graph is constructed for the cultural industry text, extracting entities and relationships with supervision. The encoding and decoding layers are optimised, and the knowledge fusion module incorporates attention. Article information is condensed and filtered, and the classifier calculates category probability. The experimental results show that in the loss function value test, in the validation data, our research method drops to the lowest value of 0.007 after about 25 iterations at the fastest, which is much lower than the lowest value of TextCNN about 0.038. When F1 value is tested in the validation data, when the average text length of our research method increases to 35 byte, the highest F1 value reaches 0.88. Our research demonstrates effective text classification in the cultural industry with higher efficiency.

Keywords: cultural industry; text classification; knowledge graph; attention mechanism; knowledge fusion.

DOI: 10.1504/IJWET.2024.139844

International Journal of Web Engineering and Technology, 2024 Vol.19 No.2, pp.127 - 147

Received: 10 Oct 2023
Accepted: 03 Jan 2024
Published online: 08 Jul 2024 *

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