应用场景驱动的AI赋能生物制造领域技术主题探析与演化

doi:10.13560/j.cnki.biotech.bull.1985.2026-0227

摘要/Abstract

摘要：

目的生物制造是新质生产力培育的重要赛道，AI技术的赋能正成为推动生物制造领域技术创新的关键因素，探究AI赋能生物制造领域技术与应用场景之间的主题关联关系及演化趋势，有助于揭示技术与应用场景的交叉融合态势，精准锚定未来科技发展方向和技术发展潜能。方法提出知识增强BERTopic主题模型的AI赋能生物制造领域技术和应用场景主题关联与演化框架，揭示面向应用场景的人工智能技术发展轨迹。首先，采用LLM提示词识别论文和专利文本中的技术实体和场景实体，并利用SBERT-Kmeans实体对齐方法，实现实体表述的语义一致性；然后，运用BERTopic模型对融合后的领域知识进行主题分析，识别技术和应用场景研究主题；最后，构建技术与应用场景的关联关系，引入时间维度发现技术、应用场景及“技术-场景”主题的动态演化趋势，并对AI赋能生物制造领域热点研究主题进行演化趋势分析。结果 AI赋能生物制造领域主要包含7大AI技术研究主题和6大应用场景研究主题，传统机器学习模型和神经网络模型占据核心地位，AlphaFold模型和Transformer模型等新技术近年来受到较多关注，药物发现与设计和蛋白质结构预测与设计成为未来的研究热点与趋势。结论知识增强BERTopic主题模型有助于提升领域主题识别的准确性和偏向性，为产业规划和创新战略制定提供决策支撑。

关键词: 生物制造, AI技术, 应用场景, BERTopic, 大语言模型

Abstract:

Objective Biological manufacturing is an important track for cultivating new quality productive forces, and the empowerment of AI technology has become a key factor in driving technological innovation in the field of biological manufacturing. Exploring the thematic relationships and evolutionary trends between AI- empowered technology and application scenarios in the field of biological manufacturing can help reveal the cross-fusion trend between technology and application scenarios, thereby accurately anchoring the future direction and potential of technological development. Method We proposed a knowledge-augmented BERTopic topic model framework for AI-enabled bio-manufacturing to elucidate the technological and application scenario themes, their associations, and evolution, thereby revealing the developmental trajectory of AI technologies tailored to application scenarios. Firstly, LLM prompt was used to recognize technical entities and scene entities in papers and patent texts, and the SBERT-Kmeans entity alignment method was employed to achieve semantic consistency in entity representation. Then, the BERTopic model was used to conduct topic analysis on the fused domain knowledge, identifying technology and application scenarios for research topics. Finally, we established the correlation between technology and application scenarios, introduced the time dimension to discovered the dynamic evolution trends of technology, application scenarios, and the “technology-scenario” theme, and analyzed the evolution trends of hot research topics in AI-enabled biomanufacturing. Result The field of AI-enabled biomanufacturing mainly includes 7 research topics on AI technology and 6 research topics on application scenarios. Machine learning models and neural network models occupy a core position, while new technologies such as AlphaFold model and Transformer model have received more attention in recent years. Drug discovery and design, as well as protein structure prediction and design, have emerged as future research hotspots and trends. Conclusion The knowledge-augmented BERTopic topic model helps to improve the accuracy and bias of domain theme recognition, and the research conclusions provide decision-making support for industrial planning and innovation strategy formulation.

Key words: biomanufacturing, AI technologies, application scenarios, BERTopic, large language models

李贞贞, 高倩, 钟永恒, 王辉, 刘佳. 应用场景驱动的AI赋能生物制造领域技术主题探析与演化[J]. 生物技术通报, doi: 10.13560/j.cnki.biotech.bull.1985.2026-0227.

LI Zhen-zhen, GAO Qian, ZHONG Yong-heng, WANG Hui, LIU Jia. Exploration and Evolution of Technology Themes in AI-empowered Biomanufacturing Driven by Application Scenarios[J]. Biotechnology Bulletin, doi: 10.13560/j.cnki.biotech.bull.1985.2026-0227.

图/表 7

图1 研究技术路线

Fig. 1 Technology route of this research

图2 AI赋能生物制造领域科技论文发表与专利文献申请时间序列趋势

Fig. 2 Time series trends of AI-empowered scientific and technological paper publications and patent literature applications in the field of biomanufacturing

图3 AI赋能生物制造领域二维可视化分布图A：AI技术；B：应用场景

Fig. 3 Two‑dimensional visualization distribution map of the AI- empowered biomanufacturing fieldA: AI technologies; B: application scenarios

图4 AI赋能生物制造领域主题层次聚类图A：AI技术；B：应用场景

Fig. 4 Hierarchical clustering diagram of research themes in AI‑ empowered biomanufacturingA: AI technologies; B: application scenarios

图5 AI赋能生物制造领域“技术-场景”主题关联图

Fig. 5 "Technology-Scene" theme correlation diagram in AI‑empowered biomanufacturing

图6 AI赋能生物制造领域主题演化趋势（2014-2025年）A：AI技术主题；B：应用场景主题；C：“技术-场景”主题

Fig. 6 Evolutionary trends of research themes in AI-empowered biomanufacturing (From 2014 to 2025)A: AI technology topics; B: application scenario topics; C: “technology-scenario” topics

图7 AI赋能生物制造领域各应用场景视角下技术主题演化趋势A：药物发现与设计；B：生物元器件智能设计与合成；C：智能生物过程控制与优化；D：人工细胞工厂智能设计与优化；E：生物系统与生物分子智能设计与再造；F：生物文本挖掘与知识发现

Fig. 7 Evolutionary trends of technology themes from the perspective of various application scenarios in AI‑empowered biomanufacturingA: Drug discovery and design; B: intelligent design and synthesis of biological components; C: intelligent control and optimization of biological processes; D: intelligent design and optimization of artificial cell factories; E: intelligent design and reconstruction of biological systems and biomolecules; F: biological text mining and knowledge discovery

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