Performance of precision poverty alleviation through circular economy based on RS-SVM model in the context of rural revitalization

Abstract

With the implementation of the rural revitalization strategy, the precision poverty alleviation through circular economy has become an important means of promoting rural development. In order to study the performance of circular economy precision poverty alleviation in rural revitalization, a performance research method based on Support Vector Machines is proposed. During the process, the rough set was introduced for the hidden feature analysis, and a precise poverty alleviation performance indicator system was established. Afterwards, the performance evaluation model was constructed and the genetic algorithm was introduced for parameter optimization. Finally, the performance of the research method was tested. The experimental results show that the research method only requires 15 evolutions to achieve a fitness value of approximately 0.021. In the test of the calculation results, the research method only had 2 points on the test set that differed by more than 0.2 from the standard value. In the F1 test, the research method increased to approximately 97.6 after 200 iterations on the test set. When conducting empirical analysis, the evaluation results obtained by the research method are basically consistent with the expert evaluation results. These results indicate that the research method has good performance and can accurately evaluate the performance of precision poverty alleviation, providing reference for poverty alleviation work.