Abstract: This study proposes a TCM culture communication data clustering division and classification method that is based on an enhanced DBSCAN clustering algorithm and an ELM model. The objective is to address the issue of Traditional Chinese Medicine (TCM) culture communication image role product design. Firstly, for the problem of extracting feature vectors of TCM cultural communication, we analyse the path of communication role product design, design the product design scheme of TCM cultural communication image role, and extract the feature vectors of TCM cultural communication; secondly, for the problem of clustering and classifying the health data of TCM, we propose a method of clustering and classifying the health data of TCM based on the SCSO-DBSCAN clustering algorithm by combining the DBSCAN clustering algorithm with the sandcat swarm optimization algorithm. Finally, the TCM cultural dissemination data clustering classification and classification methods are tested and analyzed using TCM cultural dissemination data. This problem of TCM health data clustering classification is addressed by combining the ELM network algorithm, and a classification method of TCM cultural data dissemination based on the ELM model is proposed. The experimental results demonstrate that the method proposed in this study enhances the accuracy of TCM health data clustering division and also improves the accuracy of TCM cultural data communication classification, in comparison to other algorithms used for TCM cultural communication data clustering division and classification.

Xiaoxiao Li, Libo Wan and Xin Gao, “DBSCAN Algorithm in Creation of Media and Entertainment: Drawing Inspiration from TCM Images” International Journal of Advanced Computer Science and Applications(IJACSA), 16(5), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160553

@article{Li2025,
title = {DBSCAN Algorithm in Creation of Media and Entertainment: Drawing Inspiration from TCM Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160553},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160553},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {5},
author = {Xiaoxiao Li and Libo Wan and Xin Gao}
}