Abstract

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.

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