Precise assistance strategies for rural revitalization industry based on big data analysis

Abstract

In the promotion of the rural revitalization strategy, although industrial assistance is the core path for farmers to increase their income and consolidate poverty alleviation, the traditional model relies on experience-based judgment, and the matching degree between the causes of poverty and the assistance needs is low. Therefore, this research proposes a precise industrial assistance model for rural revitalization based on big data analysis. The research first focuses on the algorithm of integrating association rules and K-means, mining the multidimensional characteristics of precise industrial assistance targets, and completing the refined clustering of assistance groups; On this basis, further collaborative filtering algorithms are introduced, relying on the similarity of group characteristics and historical assistance experience, to construct a precise assistance model for rural revitalization industry, and ultimately achieve intelligent matching and dynamic adjustment of assistance strategies. Findings denote that the fusion algorithm can accurately identify all 7 clustering clusters in the manual testing dataset. In the context of feature recognition for impoverished households, the accuracy of the algorithm remains stable within the range of 92%–98%. In large-scale testing of the Letter Recognition dataset, the algorithm runs for only about 120 s, which is significantly optimized compared to traditional algorithms, effectively reducing time complexity and demonstrating powerful processing performance for high-dimensional big data. The practical application test utilized the dynamic management database for registered poor households of a certain provincial poverty alleviation office from 2022 to 2023. The test showed that that the similarity between impoverished households presents a differentiated distribution, with the similarity between impoverished households 1 and 2 reaching 0.8250, which can be used as a precise assistance for similar groups. In summary, the research-constructed precision assistance model for rural revitalization industry based on big data analysis can provide a scientific and systematic implementation path for precision assistance in rural revitalization industry. It has important theoretical value and practical guidance significance for improving the efficiency of poverty alleviation resource allocation and enhancing the effectiveness of assistance measures.