Design of Rural Revitalization Model Classification System based on Machine Learning

Pengcheng Yang; Zhan Wen; Cheng Zhang; Xiaoming Zhang; Dehao Ren

doi:10.18535/ijsrm/v11i11.ec01

Abstract

This study focuses on rural revitalization in different regions, collected data on different successful cases of rural revitalization, and built machine learning models based on these data. During the study, the collected raw data were first analyzed and processed to facilitate the training of the machine learning model. Subsequently, machine learning algorithms were utilized to train the data into the model, and then predicted results were compared with actual results to select the machine learning model that best matches the actual results. Textual data came from websites such as the Rural Revitalization Bureau. The primary aim of this study is to assist grassroots staff in initially determining revitalization strategies and providing certain locally feasible revitalization programs to promote subsequent work. The remainder of this paper is organized as follows: The second part introduces research on model structure, detailing research methods and theoretical knowledge. The third part presents the experimental procedure. The fourth part analyzes and summarizes experimental results.

Keywords

Rural RevitalizationMachine LearningText Data AnalysisCrawler Technology

References

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[refR-5] Dai Chengyingzi.Identification of successful entrepreneurs in rural areas based on machine learning[D], Master's Dissertation of Chongqing University of Technology,2022Google Scholar ↗

[refR-6] Liu Zhiming, Liu Lu, Empirical Study on Chinese Weibo Emotion Classification Based on Machine Learning [J] Computer Engineering and Applications. 1002-8331 (2012) 01-0001-04Google Scholar ↗

[refR-7] Use machine learning techniques to classify and predict the body density of states and chemical properties. AP. Mathematical compression.412: 126587, 2022.Google Scholar ↗

[refR-8] Yang Jian Feng,Qiao Pei Rui,Li Yong Mei,Wang Ning. A Review of Machine Learning Classification Problems and Algorithm Research. Statistics & Decision. 1002-6487（2019）06-0036-05Google Scholar ↗