Using BP neural network and entropy weight method to analyze computer technology applications in China's agricultural modernization

Abstract

Since the 1850s, agricultural transformation has significantly impacted society and continues to do so with the new round of agricultural revolution. Despite remarkable achievements in agricultural modernization, China also faces contradictions and challenges. The government has issued documents to support agricultural modernization and promote comprehensive agricultural system construction. This article analyzes provincial agricultural modernization level index using catastrophe progression, Entropy weight combination expectation, and BP neural network methods. It constructs a regional economic indicator system and builds a coupling model between agricultural modernization and economic growth based on coupling theory. The article visualizes agricultural modernization levels, indicating regional imbalance in China's agricultural modernization development. The eastern, southern, and coastal regions have better overall and various indicators than the western, northern, and border regions. There is a situation where provinces and cities with the same centers are decreasing towards the surrounding areas. Therefore, suggestions such as rationally allocating agricultural development resources and suitably planning agricultural input and output structure are put forward. Using coupled rationality theory to construct a coupling model between modern agriculture and the regional economy, it was found that there is imbalance in the level of agricultural modernization between regions and disharmony between agricultural modernization and the economic level within regions.