用大模型从房产问卷中自动提取关键信息，准确率达82%。
Structured Data Extraction from Real Estate Documents using Clustering, Classification, and Large Language Models
- 分三类处理文档，用大模型提取35项属性，输出结构化数据。
- 处理2781份文档，生成2766条唯一房源记录，质量验证通过。
- 适合做房产数据清洗、市场分析的团队参考使用。
房产平台通过API暴露结构化元数据，但最丰富的房源信息（如产权状态、结构状况、水电供应、供暖系统）仍藏于附件问卷文档中，现有系统无法规模化处理。这些文档类型多样：部分为可选文本的电子文档，部分为扫描件，还有含复选框等复杂布局的文档，传统文本提取方法失效。本文提出端到端流水线，从真实平台通过逆向工程的REST API获取3965份问卷文档。首先将文档分类为三类（纯文本、扫描件、特殊字符），然后对符合条件的文档使用DeepSeek R1大模型提取35个预定义属性，提示其返回结构化JSON。全部2781份提交文档成功处理，生成2766条唯一房源记录。下游验证显示：余弦相似度匹配的杰卡德一致性得分为0.82，K-Means聚类得到轮廓系数0.2088的可解释市场细分。结果表明，该方法在大规模下可行且可靠。
原文摘要 · Abstract (English)
Real estate property listings expose structured metadata through the API. Still, the richest property-level information (i.e., legal status, structural condition, utility supplies, heating systems) sits in attached questionnaire documents that no automated system currently processes at scale. These documents are heterogeneous. Some are digitally generated with selectable text, others are scanned physical forms. There are even more complex layouts that contain checkbox annotations that defeat conventional text extraction. In this paper, we present an end-to-end pipeline for acquiring, classifying, and extracting structured data from selectable text documents. The pipeline was applied to 3965 questionnaire documents collected from a live property platform via reverse-engineered REST APIs. First, we classified each document into one of three structural categories (text_only, scanned, and special_char), then extracted 35 predefined property attributes from eligible documents using DeepSeek R1 as the Large Language Model, prompted to return a structured JSON object. All 2781 submitted documents were processed successfully, producing a final dataset of 2766 unique property records. Downstream validation confirmed the data quality. Cosine similarity matching achieves a Jaccard consistency score of 0.82, and K-Means clustering produces interpretable market segments with a silhouette score of 0.2088. Results show that the proposed extraction from each property document is both feasible and reliable at this scale.
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