Education, Science, Technology, Innovation and Life
Open Access
Sign In

Research on Food Security Evaluation Model based on data implicit Distribution Mining algorithm

Download as PDF

DOI: 10.23977/agrfem.2021.040106 | Downloads: 8 | Views: 381


Weiyao Li 1, Ziqi Bao 2, Jinyan Zhang 1


1 School of Communication Engineering, Beijing Jiaotong University, Beijing 100044
2 School of Economics and Management, Beijing Jiaotong University, Beijing 100044

Corresponding Author

Weiyao Li


In recent years, the global food crisis has become increasingly serious. Therefore, we hope to build a new model of grain supply system to maximize the overall benefit of grain under the constraints of objective conditions. We first collect agricultural data from countries with different levels of development. Then, we use entropy method and TOPSIS model to construct the evaluation model of grain supply system, and compare the internal analysis and weight of different data. After comprehensively measuring the four aspects of grain production conditions, environmental sustainability, fairness and profit, we apply the model to value evaluation. The results show that sustainability and equity are important indicators.


food security, entropy method, TOPSIS, comprehensive evaluation model


Weiyao Li, Ziqi Bao, Jinyan Zhang. Research on Food Security Evaluation Model based on data implicit Distribution Mining algorithm. Agricultural & Forestry Economics and Management (2021) Vol. 4: 28-32. DOI:


[1] Luo, N., Lennon, O., Liu, Y., 2021, A Conceptual Framework to Analyze Food Loss and Waste within Food Supply Chains: An Operations Management Perspective.13 (2), pp.927-927.
[2] Fahad, S., Sonmez, O., Saud, S., Wang, D., Wu, C., Adnan, M., Turan, V., 2021, Developing Climate Resilient Crops: Improving Global Food Security and Safety. 
[3] Shi, X., Wei, W., Fu, Z., Gao, W., Zhang, C., Zhao, Q., Deng, F., Lu, X., 2019, Review on carbon dots in food safety applications. Talanta, 194, pp.809-821.
[4] Yu, X., Ge, J., Song, W., 2002, Application of Rotating Regression Analysis in Increasing Crop Yield. Mathematical Statistics and Management, 2004(2), pp.7-9.
[5] Mo, H., 1980, Planting density and crop yield-the quantitative relationship between yield and density and its analysis (continued). Journal of Jiangsu Agricultural College, 1980(3), pp. 12-26.

Downloads: 594
Visits: 30226

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.