The Logical Mechanism and Practical Progress of Digital Economy Contributing to Rural Revitalization in the Context of Big Data

Abstract

This paper proposes spatial data mining and clustering analysis techniques based on big data and uses four distance similarity calculation methods, namely, Euclidean distance, Manhattan distance, Ming’s distance, and Ma’s distance, to constrain spatial clustering similarity metrics. Combine the K-means algorithm and FCM clustering algorithm to calculate the center value of fuzzy clustering, use the additive synthesis method to solve the combination weights, and construct the combination weighted distance, fuzzy C-mean algorithm model. Research on the mechanism and path of digital economy-enabled rural revitalization through the C-FCM model, and propose three correlation hypotheses for this paper. Empirical experiments are conducted to verify the authenticity of the hypotheses proposed in this paper and the use effect of the constructed model. The experimental results show that the Moran index range of digital economy and rural revitalization is [0.06, 0.12], [0.05, 0.16], respectively, and there is a significant aggregation phenomenon of the two under the geographic matrix. In the analysis of the spatial spillover effect, the values of spatial autoregressive coefficients of rural industrial revitalization are all >0, with significance p>0.05. The digital economy has a positive effect on rural revitalization in neighboring areas through the spatial spillover effect. In the clustering sample extraction test, the accuracy is most stable when the number of samples is 2200, and the accuracy rate is as high as 79.66%.

Keywords

• g-fcm model
• data visualization
• cluster analysis
• spatial data mining
• rural revitalization
• 68p01