Design of Rural Revitalization Model Classification System based on Machine Learning

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.