Digital Management Mode of Real Estate Marketing based on Big Data and Artificial Intelligence

Abstract

To cope with the pressure on sales information processing as the real estate industry grows, the study builds a real estate digital marketing management system design based on the analysis of real estate marketing needs to meet the needs of real estate marketers for digital information processing, and builds a hybrid recommendation model using a combination of Gradient Boosting Decision Tree (GBDT) technology and Logistic Regression (LR) to accurately recommend real estate potential purchase users. The GBDT-LR model performance test results show an accuracy of 94.63% and a regression rate of 94.82%, which is particularly good in terms of classification accuracy, and the system CPU occupancy rate basically stays below 30% during the whole script running period, and the system still maintains good system stability when the TPS user concurrency is 150, and it’s using experience is better. The comparison of the ROC curve of the GBDT-LR model shows that the GBDT-LR model's accuracy is as high as 92%, which is better than the performance of most of the classification models, and it can meet the practical application requirements of the real estate industry and provide a good solution for the real estate industry. It can meet the actual application requirements of the real estate industry and provide a scientific and systematic digital management solution for the real estate industry.