Research on the Evolutionary Pathway of Science–Technology Topic Associations: Discovering Collaborative and Symmetrical Effects

This study employs text mining techniques to conduct a systematic quantitative analysis of cybersecurity-related scientific publications and technological research. It aims to break through the limitations of traditional unidirectional evolutionary research, reveal the knowledge evolution rules between scientific theories and technical practices in this field, and provide valuable references and decision-making support for optimizing the collaborative innovation ecosystem. Firstly, we took academic papers and patent research on cybersecurity from 2005 to 2024 as the research objects and divided them into ten stages according to the time series. Subsequently, we identified scientific and technological topics and formed science–technology topics to assess their similarity. Then, we selected 3040 pairs of collaborative topic pairs and categorized them into three distinct groups: weak, moderate, and strong correlation. Finally, we constructed a science–technology topic association evolution atlas and analyzed the types of evolutionary pathways of topic associations and their mechanisms of action accordingly. The results demonstrate five evolutionary patterns in science–technology topic associations: division, merging, inheritance, co-occurrence, and independent development. Additionally, the science–technology topics demonstrate a high degree of collaboration, exhibiting a collaborative effect of “initial accumulation–fluctuating differentiation–deep collaboration”. Meanwhile, the correlation evolution of strongly related science–technology topics presents a symmetrical effect of “technology–science–technology” and “science–technology/technology–science”.