A Method for Planning the Dissemination Path of Traditional Chinese Medicine Culture Based on the Optimized Ant Colony Algorithm

Strategic planning improves TCM cultural transmission efficacy, reliability, and impact. Many systems use heuristic or rule-based approaches, which have drawbacks such as path redundancy, low adaptation, and limited scalability in non-static networks. To address these constraints, we suggest RACO-TCM, or Reinforced Ant Colony Optimization for TCM Dissemination. This novel algorithmic distribution technique uses Ant Colony Optimization and reinforcement learning to create adaptable reward-driven cultural routes. The framework outperforms standard ant colony optimization because it uses dynamic pheromone updates, reinforcement-based exploration, and redundancy-aware heuristics to improve global search, convergence time, and robustness to local optimal solutions. We quantitatively assessed RACO-TCM against other methods and found that it increased cultural diffusion efficiency by 18.6% and reduced repeated routes by 12.3%. Creating a vast and instructive TCM knowledge graph with over 46,000 prescriptions, 8,000 herbs, and 25,000 chemical compounds achieved this. Overall, the TCM transmission technique is adaptive, scalable, and culturally consistent. It is used to manage business and TCM tourism, promote healthcare, digital education, and cultural services in smart cities.

@article{Guo2025,
  title     = {A Method for Planning the Dissemination Path of Traditional Chinese Medicine Culture Based on the Optimized Ant Colony Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {11},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Qian Guo and Ying Ma},
  doi       = {10.14569/IJACSA.2025.0161110},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161110}
}