The analysis and prediction modeling for the visual presentation of provincial big data concerning realty transaction and registration is of great significance for studying the layout trend of China’s urban-rural construction and regional economy. It shows the temporal and spatial evolution of urban construction and development indicators and supports scientific decision-making and macro-control. The prediction modeling of these economic activity data involves the understanding of the state evolution of things with complex factors and without clear mathematical expression. Thus, inspired by the successful applications of modern artificial intelligence-deep neural network technology in similar complex scenes, we intend to establish a macro visual prediction system of heat maps of provincial realty big data by related long short-term memory (LSTM) model and fully connected layer (FC) technology. The main system construction practice of this paper is that we utilize the big data from the legal business of realty which are accumulated in Guangdong Province (not including Dongsha Islands) over the years to implement the functions of modeling and predicting regional year-end geographic heat maps of two basic indicators, i.e., the number and the total area of existing realty units, regarding the temporal years when the realty was built in each city. This study creatively puts forward the overall prediction modeling and calculation framework of “grid cumulative prediction + incremental prediction correction for a city”. It increases the optimization options of grid granularity adjustment and local-global prediction correction for the artificial intelligence modeling and prediction of provincial realty big data and improves the applicability of the prediction model. Application analyses show that the calculation results of the modeling prediction system are reasonable and practical.