The academia and industry are characterized by a reciprocal shaping and dynamic feedback mechanism. Despite distinct institutional logics, they have adapted closely in collaborative publishing and talent mobility, demonstrating tension between institutional divergence and intensive collaboration. Existing studies on their knowledge proximity mainly rely on macro indicators such as the number of collaborative papers or patents, lacking an analysis of knowledge units in the literature. This has led to an insufficient grasp of fine-grained knowledge proximity between industry and academia, potentially undermining collaboration frameworks and resource allocation efficiency. To remedy the limitation, this study quantifies the trajectory of academia-industry co-evolution through fine-grained entities and semantic space. In the entity measurement part, we extract fine-grained knowledge entities via pre-trained models, measure sequence overlaps using cosine similarity, and analyze topological features through complex network analysis. At the semantic level, we employ unsupervised contrastive learning to quantify convergence in semantic spaces by measuring cross-institutional textual similarities. Finally, we use citation distribution patterns to examine correlations between bidirectional knowledge flows and similarity. Analysis reveals that knowledge proximity between academia and industry rises, particularly following technological change. This provides textual evidence of bidirectional adaptation in co-evolution. Additionally, academia's knowledge dominance weakens during technological paradigm shifts. The dataset and code for this paper can be accessed at https://github.com/tinierZhao/Academic-Industrial-associations.

翻译：学术界与产业界呈现出相互塑造与动态反馈的特征。尽管存在制度逻辑的差异，二者在合作发表与人才流动方面已形成紧密适应，展现出制度分化与深度协作之间的张力。现有关于知识邻近性的研究主要依赖合作论文或专利数量等宏观指标，缺乏对文献中知识单元的分析。这导致对产学间细粒度知识邻近性的把握不足，可能影响合作框架与资源配置效率。为弥补这一局限，本研究通过细粒度实体与语义空间量化产学协同演化轨迹。在实体度量部分，我们通过预训练模型提取细粒度知识实体，使用余弦相似度衡量序列重叠度，并借助复杂网络分析拓扑特征。在语义层面，我们采用无监督对比学习方法，通过测量跨机构文本相似性来量化语义空间的收敛性。最后，我们利用引文分布模式检验双向知识流与相似性的关联。分析表明产学知识邻近性呈上升趋势，技术变革后尤为显著。这为协同演化中的双向适应提供了文本证据。此外，在技术范式转换期间，学术界的知识主导地位有所减弱。本文数据集与代码可通过 https://github.com/tinierZhao/Academic-Industrial-associations 获取。