Housing prices has always been the most concerning topic in society.Predicting the trend of housing prices and providing reference for homebuyers has always been a research hotspot in the real estate industry and related academic fields..In response to the problem of multiple variables and high dimensionality in the dataset of housing price prediction,this article calculates the Pearson coefficient between multiple housing features and housing prices,removes redundant housing features,and effectively reduces the dimensionality of the housing feature dataset.In the process of data preprocessing,a Catboost category variable processing method is used to minimize the information loss.In view of the problem of overfitting and poor generalization ability of the prediction model,a LSTM-StackingCXR model is established by combining stackingstrategy with Catboost,XGBoost and random forest model.The experimental results show that the prediction results of LSTM-StackingCXR model has significantly improved compared to the prediction results of multiple existing models.