With rapid development of e-commerce on the Internet, more sellers enter the C2C ( customer to customer) industry, on the one hand they promote the prosperity of online transactions, and on the other hand it increases the difficulty of buyer to make a reasonable choice of a good trading partner. Based on the analysis of indicators that reflected the seller' s information, using custom network data capture tools, this paper randomly chose 40 indicator data of seller for analysis. By using principal component analysis (PCA) , calculated and processed the seller's credit indicators. After analyzing the results, extracted the main factors which affected the choice of credit. Eventually found 13 evaluation factors which played an important role of credit rating. Through this method, evaluated the sellers' credit and obtained a rank. It can provide a reference for distinguishing different degrees of the seller's credit information.%随着互联网上电子商务的高速发展,越来越多的商品销售方进入C2C这一行业,一方面促进了网络交易的繁荣,另一方面也增加了买方合理选择良好交易对象的难度.分析能够反映卖方信息的指标因素,使用自定义的网络数据抓取工具,随机选取40条卖方的指标数据用做分析.使用主成分分析法对这些卖方的信用指标进行计算和处理,并对结果进行分析,提取出影响信用选择的主要因素,最终得到13项对信用评价起重要作用的评价因素；应用该方法对卖方进行评价得到评价排序,为区分和筛选不同信用程度的卖方信息提供了参考.
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