Intelligent Algorithm-Based Multiple Logic Transmission Paths for Common Wealth in Ideological and Political Courses in Colleges and Universities

This study utilizes panel data from 30 provinces (regions and municipalities) spanning the years 2015–2024 and employs dynamic QCA methods to identify the dissemination pathways for promoting common prosperity in higher education ideological and political courses. To validate the reliability of this dissemination pathway, a parameter inversion method based on neural networks was designed. First, a modified SIR epidemic model was selected to construct a common prosperity dissemination model. Then, MATLAB R2014 was used to perform parameter inversion for the dissemination model. Finally, model fitting and trend prediction analysis were conducted for the dissemination of common prosperity. Taking institutional development data as an example, the more dispersed the distribution of institutional development values, the fewer citizens holding extreme opinions during the early stages of common prosperity, and the more stable the dissemination of common prosperity. Validating the extracted real-world data, the model established in this study aligns with the actual trend changes, providing valuable insights for researching the dissemination trends of common prosperity.