Data fusion-driven difference analysis of farming culture between China and Japan

Abstract

The difference in farming culture is the basis for the exchange, reference, and integration of agricultural culture. We propose a method based on data fusion and extraction method for analyzing differences in farming culture between China and Japan in this paper. Specifically, we designed a farming culture difference analysis model based on deep learning technology and improved algorithm. We combined deep neural network and particle swarm algorithm to design and improve the analysis model of farming culture difference. Firstly, we introduce in detail the designed analysis model based on deep learning, namely BP neural network. Secondly, we adopt the particle swarm algorithm (PSO) to improve and upgrade the defects of the BP neural network model. The experimental and comparative analysis of the results shows the characteristics of China's vast land and abundant resources and the characteristics of Japan, an island country with limited cultivated land.