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Chinese ethnic minority book classification by large language models within CLC
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AI
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DOI：10.1108/EL-04-2025-0161.png)
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Abstract
En 中文
PurposeThis study evaluates the capabilities and limitations of large language models (LLMs) in classifying Chinese ethic minority books under the scheme of Chinese Library Classification.Design/methodology/approachA test collection of Chinese ethnic minority bibliographic records was constructed, and prompt engineering was used to compare the classification performance of DeepSeek-v3 and ChatGPT-4o under two input scenarios: title + abstract and title only. By designing evaluation metrics that include accuracy, granularity and error-type analysis, this study systematically evaluates the performance differences between the models, diagnoses the causes of errors and proposes improvement strategies.FindingsExperimental results show both models performed well in the broad category classification of Chinese ethnic minority books, with DeepSeek-v3 exceeding 80% accuracy. Incorporating abstracts further improved accuracy and prompted longer, more detailed classification codes. However, accuracy declined for both as classification codes grew more specific. DeepSeek-v3 significantly outperformed ChatGPT-4o, achieving an overall accuracy of 40.78% and 33.50% with and without abstracts, respectively, while ChatGPT-4o remained below 6%. On the basis of classification error analysis, this study proposes improvements in classification system design, model capability enhancement and human-artificial intelligence (AI) collaboration to guide practical improvements in organizing ethnic minority resources.Originality/valueCombining librarianship, ethnography and artificial intelligence, this study is the first to compare the classification ability of different large-language models for Chinese ethnic minority books. It reveals cultural limitations in knowledge organization systems, identifies the capability threshold of LLMs in cultural context processing and establishes an empirical basis for developing culturally-aware AI governance frameworks.
Keywords:
Large language models
Ethnic minorities
Chinese library classification
Cultural sensitivity
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