Analysis of Guangdong-Hong Kong-Macao Greater Bay Area’s Economic Growth Trend Based on Big Data Mining

Abstract

For the sake of improving the optimal management and dispatching ability of Guangdong-Hong Kong-Macao Greater Bay Area’s economy, it is essential to optimize and predict the growth trend of the Greater Bay Area’s economy, put forward the optimization prediction method of the Greater Bay Area economic growth trend based on 3500 mining, and construct the economic growth model of statistical sequence distribution. Big data mining method is chosen to model the big data statistical information of the area’s economic growth, extract the characteristic quantity of the association rules of the big data economic growth trend, use the fuzzy fusion clustering method to carry on the automatic clustering processing to the economic growth trend, and establish the optimal iterative model of the prediction of the economic growth trend. Combined with adaptive optimization algorithm, the Greater Bay Area’s economic growth trend is optimized and predicted. The simulation outputs show that the method has good adaptability to predict economic growth trend of the area we talked about, and has high accuracy in predicting growth trend, which improves the adaptive scheduling and management ability of the economy in the bay area.