融合大语言模型的区块链开源项目主题挖掘与演化分析

doi:10.3969/j.issn.0255-8297.2026.05.001

Abstract

Abstract: Based on GitHub blockchain open-source projects from 2021 to 2024, this paper proposed a topic identification framework for open-source projects that integrates a large language model with bidirectional encoder representations for topic (BERTopic). The framework first applied large language model prompt engineering to perform specific data preprocessing and text refinement for open-source projects. It then constructed a BERTopic model through word embedding, dimensionality reduction using uniform manifold approximation and projection (UMAP), and hierarchical density-based spatial clustering of applications with noise (HDBSCAN) to identify blockchain topics. Finally, topic labels for blockchain open-source projects were generated by combining “topic representation and naming using a large language model” with “expert evaluation and optimization”. Based on the topic identification results, a three-dimensional index system comprising “topic intensity-technological influence-secondary development activity” was constructed to evaluate the identified topics and analyze their evolutionary characteristics. The research shows that the blockchain open-source ecosystem presents an evolutionary path of “foundation consolidation-application expansion-intelligent integration”. In addition to identifying common technical topics around smart contracts, decentralized finance (DeFi), non-fungible tokens (NFTs), etc., the study also finds that “AI + blockchain”-related technical topics are becoming new growth points. The multidimensional index evaluation shows that the developer community continues to strengthen security foundations and toolchain development while keeping pace with technological and market trends.

Key words: blockchain, topic mining, topic evolution, open-source projects, GitHub, large language model

CLC Number:

TP391

RAO Ruirui, ZHU Li, YAN Jiaqi. Topic Mining and Evolution Analysis of Blockchain Open-Source Projects Integrating Large Language Model[J]. Journal of Applied Sciences, 2026, 44(5): 701-715.

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URL: https://www.jas.shu.edu.cn/EN/10.3969/j.issn.0255-8297.2026.05.001

https://www.jas.shu.edu.cn/EN/Y2026/V44/I5/701

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Topic Mining and Evolution Analysis of Blockchain Open-Source Projects Integrating Large Language Model

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