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.

China realize the sustainable development of socialism, and promote the integration and development of Guangdong, Hong Kong, Macao and the Gulf region. This paper analyzes the development path of the urban agglomeration in Da Wan District of Guangdong, Hong Kong and Macao under the backdrop of big data. A statistical sequence distribution model of the GDP index of the city group development in the Big Gulf Region of These city is constructed, the big data statistical information model of the GDP index of the city group development in the Big Gulf Region of These city is built by using a big data mining method, the association rule characteristic quantity of the GDP index of the city group development in the Big Gulf Region of These city is extracted, the big data of the GDP index of the city group development in the Big Gulf Region of These city under the big An optimization iteration model for the prediction of the GDP index for the development of the large bay area urban agglomeration in These city is established. Under the backdrop of big data, the development path analysis and adaptive adjustment of the large bay area urban agglomeration in These city are carried out to realize the analysis and optimization of the development path of the large bay area urban agglomeration The simulation results show that the prediction accuracy of the GDP index of These city and the gulf city assembly development is high, and the adaptability and convergence of the GDP index prediction of These city and the gulf city assembly development are improved.