This paper uses Python third-party library Requests to crawl the housing data of second-hand housing websites, uses Pandas library to structurally process the crawled data, and uses Pyechards library to conduct multi-dimensional in-depth analysis and visual presentation of second-hand housing data. From a large number of online data, this paper analyzes the distribution of secondhand houses, market hot spots and price trend in Nanjing, helps buyers and real estate agents to make efficient decisions in market activities, and provides reference for government intervention and supervision of the second-hand housing market.