学术界与产业界呈现出相互塑造与动态反馈的机制。尽管二者具有不同的制度逻辑，但在合作发表与人才流动方面已高度适应，体现出制度差异性与深度协作之间的张力。现有对二者知识邻近性的研究主要依赖于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，导致对学术界与产业界之间细粒度知识邻近性的理解不足，可能削弱协作框架设计与资源配置效率。为弥补这一局限，本研究通过细粒度实体与语义空间量化 academia-industry 共同演化轨迹。在实体测量部分，我们利用预训练模型提取细粒度知识实体，采用余弦相似度衡量序列重叠，并通过复杂网络分析考察拓扑特征；在语义层面，我们采用无监督对比学习，通过测度跨机构文本相似性来量化语义空间中的收敛程度；最后，利用引文分布模式检验双向知识流动与相似性之间的相关性。分析表明，学术界与产业界的知识邻近性呈上升趋势，尤其在技术变革之后更为显著，为共演化过程中的双向适应提供了文本证据；此外，在技术范式转变期间，学术界的知识主导地位趋于减弱。本文所用数据集与代码可于 https://github.com/tinierZhao/Academic-Industrial-associations 获取。

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.