现代信息科技2026，Vol.10Issue(5)：46-50,5.DOI:10.19850/j.cnki.2096-4706.2026.05.009
基于机器学习的二手房价格预测研究
Research on Second-hand House Price Prediction Based on Machine Learning
摘要
Abstract
Second-hand housing transaction price evaluation has important reference value and provides a basis for the decision-making of governments,house buyers,sellers and real estate agencies.This paper takes second-hand house prices in Taiyuan city,Shanxi Province as the research object,constructs prediction models and selects the optimal scheme by comparing model effects.Firstly,Web crawler technology is used to obtain second-hand housing data of Taiyuan from Lianjia website,and 8 394 data records including 63 most representative characteristic variables are finally obtained for house price prediction.Secondly,the matplotlib library of Python is used to conduct visual research from three aspects:location characteristics,building characteristics and transaction characteristics,and the influence relationship between house prices and various variables is initially determined.Finally,in order to select the optimal model to predict the changes of second-hand house prices in Taiyuan,the CART Decision Tree model and the XGBoost model are constructed respectively.The model comparison results show that the XGBoost model has high accuracy and is more suitable for second-hand house price prediction.关键词
二手房价格/CART决策树模型/机器学习/XGBoost模型Key words
second-hand house price/CART Decision Tree model/Machine Learning/XGBoost model分类
信息技术与安全科学引用本文复制引用
朱丽丽,李岩松..基于机器学习的二手房价格预测研究[J].现代信息科技,2026,10(5):46-50,5.基金项目
2025年山西省高等学校科技创新项目(2025W066) （2025W066）
2025年山西省哲学社会科学规划课题(2025QN274) （2025QN274）
2024山西能源学院教育教学改革和实践项目(NJ202416) （NJ202416）
