A Quantitative Modeling Analysis Method of Mental Models Combined with Link Tables

As the basic structure of human cognition, mental models manifest the structure of people's cognition, experience and knowledge of the real world. Most of the existing studies focus on the structure and content of mental models, and a more objective and comprehensive approach to modeling mental models has not been proposed. To address this issue, firstly, the unstructured text data is transformed into a structured link matrix from link relationship representation for further computational work. A page ranking algorithm is also introduced to identify the important nodes of the mental model network to achieve key intent capture and design concept transformation. After that, a content network-based clustering analysis is used to identify the evolutionary process of users' mental activities. Finally, a case study analysis is utilized to verify the effectiveness of this modeling and analysis method, which provides a reference for future theoretical research and application practice in related fields.