Analysis of Rural Governance and Resource Endowment Modeling Based on Association Rule Algorithm

  • Wenxiu Gan 1)College of history and sociology, Xinjiang Normal University, Urumqi 830013, China, 2)School of Marxism, Hexi University, Zhangye 734000, China
  • Kaibing Wang Police Sports Department of Shanxi police college, Taiyuan 030401, China
Keywords: association rule algorithm, rural governance, resource endowment, modeling analysis, classified governance, factors of production


At present, in the modeling and analysis of rural resource endowment, the internal relationship of elements is ignored, resulting in inaccurate judgment of governance level. Therefore, the modeling and analysis of rural governance and construction resource endowment is based on association rule algorithm. Identify the characteristics of rural governance resource elements as the information basis, design the clustering algorithm to determine the association rules and element attributes, and use the association rules algorithm to mine the internal relationship of resource endowment. Taking the information of rural governance resource endowment as the direction, the evaluation index is selected, and the rural resource endowment measurement model is constructed. The experimental results show that the modeling analysis results based on association rule algorithm are consistent with the actual governance development orientation, while the modeling analysis results based on evolution analysis algorithm and special group analysis algorithm are quite different from the actual governance development orientation. Therefore, the modeling analysis in this paper is more accurate, which is conducive to the accurate implementation of governance policies and rural planning.


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Author Biographies

Wenxiu Gan, 1)College of history and sociology, Xinjiang Normal University, Urumqi 830013, China, 2)School of Marxism, Hexi University, Zhangye 734000, China

Wenxiu Gan (1980-), female, born in Yongdeng County, Gansu Province, doctoral candidate of School of History and Sociology, Xinjiang Normal University; lecturer of Hexi University; Research area: ethnic society and culture, ideological and political education. Published several papers and participated in several projects.

Kaibing Wang, Police Sports Department of Shanxi police college, Taiyuan 030401, China

Wang Kaibing, male, Han nationality, was born in 1974, Yushe County, Jinzhong City, Shanxi Province, postgraduate, master, senior psychological consultant.title: Associate Professor, research direction: physical training educational science and police sports.

Participated in 2000 and has been working in physical training educational science and police sports. Now working in the Police Sports Department of Shanxi Police College, professional and technical police supervisor, an academic leader of the college, is hired as an instructor professor by many units in China.

Received many awards of Shanxi Public Security Department and Shanxi Police college, won the Excellent Individual of Ministry of Public Security, Excellent Correspondent of Public Security Education, Excellent Police Practical Skills Instructor in Shanxi Province, Excellent Teacher and Excellent Research Worker of Shanxi Police college, and won the Special Contribution Award of Shanxi Police college.

Participate preside over 8 the provincial and ministerial projects, participated in the compilation of 1 national planning textbook for general higher education, 7 textbooks of other categories, and published more than 40 papers and 3 invention patents.


Gao, J., Wang, H., and Shen, H. (2020, May). Smartly handling renewable energy instability in supporting a cloud datacenter. In 2020 IEEE international parallel and distributed processing symposium (IPDPS) (pp. 769–778). IEEE.

Liu, B. H., Nguyen, N. T., Pham, V. T., and Lin, Y. X. (2017). Novel methods for energy charging and data collection in wireless rechargeable sensor networks. International Journal of Communication Systems, 30(5), e3050.

Abdel-Basset, M., Manogaran, G., Gamal, A., and Smarandache, F. (2018). A hybrid approach of neutrosophic sets and the DEMATEL method for developing supplier selection criteria. Design Automation for Embedded Systems, 22(3), 257–278.

Sundhari, R. M., Murali, L., Baskar, S., and Shakeel, P. M. (2020) MDRP: Message dissemination with re-route planning method for emergency vehicle information exchange. Peer-to-Peer Network and Applications.

Chi, X., Wang, Y., Gao, J., Liu, Q., Sui, N., Zhu, J., and Zhang, H. (2016). Study of photoluminescence characteristics of CdSe quantum dots hybridized with Cu nanowires. Luminescence, 31(7), 1298–1301.

Varatharajan, R., Manogaran, G., and Priyan, M. K. (2018). A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing. Multimedia Tools and Applications, 77(8), 10195–10215.

Shakeel, P. M., Baskar, S., Fouad, H., Manogaran, G., Saravanan, V., and Xin, Q. (2020). Creating Collision-Free Communication in IoT with 6G Using Multiple Machine Access Learning Collision Avoidance Protocol. Mobile Networks and Applications, 1–12.

Nguyen, N. T., Liu, B. H., Pham, V. T., and Liou, T. Y. (2017). An efficient minimum-latency collision-free scheduling algorithm for data aggregation in wireless sensor networks. IEEE Systems Journal, 12(3), 2214–2225.

Paraskevopoulou, A. T., Nektarios, P. A., and Kotsiris, G. (2019). Post-fire attitudes and perceptions of people towards the landscape character and development in the rural peloponnese, a case study of the traditional village of leontari, arcadia, greece. Journal of Environmental Management, 241(JUL.1), 567–574.

Zhou, T., Koomen, E., and Ke, X. (2020). Determinants of farmland abandonment on the urban–rural fringe. Environmental Management, 65(3), 369–384.

Nemet, S., Kukolj, D., Ostojic, G., Stankovski, S., and Jovanovi, D. (2019). Aggregation framework for tsk fuzzy and association rules: interpretability improvement on a traffic accidents case. Applied Intelligence, 49(11), 3909–3922.

Coulibaly, L., Kamsu-Foguem, B., and Tangara, F. (2020). Rule-based machine learning for knowledge discovering in weather data. Future generation computer systems, 108(Jul.), 861–878.

Smart, P., Thanammal, K. K., and Sujatha, S. S. (2021). A novel linear assorted classification method-based association rule mining with spatial data. Sādhanā, 46(1), 1–12.

Shazad, B., Khan, H. U., Zahoor-ur-Rehman, Faroocv, M., Mahmood, A., and Mehmood, I., et al. (2020). Finding temporal influential users in social media using association rule learning. Intelligent automation and soft computing, 26(1), 87–98.

Pavithra, B., and Niranjanamurthy, M. (2020). Web page recommendation using genetic and feed forward association rule on web-log features. Journal of Computational and Theoretical Nanoscience, 17(9), 4462–4467.

Krishnamoorthy, S., and Murugesan, K. (2019). Protecting the privacy of cancer patients using fuzzy association rule hiding. Asian Pacific Journal of Cancer Prevention, 20(5), 1437–1443.

Dolejs, M., Nadvornik, J., Raska, P., and Riezner, J. (2019). Frozen histories or narratives of change? contextualizing land-use dynamics for conservation of historical rural landscapes. Environmental Management, 63(3), 352–365.

Essougong, U., Fouepe, G., and Degrande, A. (2019). Can community-based organisations deliver adequate agricultural information to farmers? evidence from rural resources centres in cameroon. Information development, 35(3), 435–446.

Roesler, T., and Hassler, M. (2019). Creating niches – the role of policy for the implementation of bioenergy village cooperatives in Germany. Energy Policy, 124(JAN.), 95–101.

Bond, A. J., Saison, C., Lawley, V. R., and O’Connor, P. J. (2019). Bridging the urban-rural divide between ecosystem service suppliers and beneficiaries: using a distributed community nursery to support rural revegetation. Environmental Management, 64(2), 166–177.

Sim H S. Big Data Analysis Methodology for Smart Manufacturing Systems. International Journal of Precision Engineering and Manufacturing, 2019, 20(10):973–982.

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