| Recently, B2C e-commerce has been made rapid development in China. B2C enterprises encourage consumers to have online reviews after purchasing goods. Online reviews facilitate the consumers to know product quality and marketing services,reduce the consumer's perceived risk and cultivate consumer trust in online shopping. This leads to the prosperity of online shopping. B2C online reviews express feelings, convey the potential demand for customers. Online reviews have become a key strategic resource and competitive factors, which is an important source of customer knowledge. Online reviews can help companies improve services and management through data mining.Online reviews that scatter in different B2C commerce website are offered by the initiative customers in an unstructured text form. It is worth of collecting, mining and managing the customer knowledge in B2C online reviews, which help enterprises gain customer reviews and service experience about goods with lower cost, to explore the potential demand of customers. But there are some shortcomings in B2C online review on the production of customer knowledge, accessing to online reviews, intelligent processing customer knowledge in reviews, and information management about customer knowledge. The dissertation studies several key issues about customer knowledge management in the B2C online reviews. The main research contents are as follows:Firstly, the hypothetical model about participation of consumer behavior online reviews built by theoretical analysis, which is a process that customer initiatively convert tacit knowledge to explicit knowledge. The dissertation analyzes the behavior of consumers'online review participation motivation and loyalty website by questionnaire survey. Discussing the B2C companies how to develop better strategies to encourage consumer reviews to contribute their knowledge.Secondly, the issue is B2C online reviews in semi-structured form webpage do not facilitate the analysis and processing which scattered in different websites. The dissertation studies information extraction about online reviews from multiple information sources website. The dissertation uses Choquet integral to improve HMM effect in web information extraction, which can extract information of goods and online reviews. The method is an effective tool to collect information for enterprises.Thirdly, the method about intelligent classification online customer reviews knowledge is studied. The dissertation establishes the classification standard of online review, improve KNN the effect of text categorization by fuzzy measures and D-S theory. Effective classifying the customer online reviews help companies get customers knowledge.Finally, enterprises need a flexible, rapid deployment plans. The dissertation combined service-oriented architecture model and ontology to study how to establish effective customer knowledge management system to manage and effectively use customer knowledge. The dissertation definite online reviews customer knowledge management system,design system architecture, and analyze workflow system.In summary, the dissertation research on several key issues about customer knowledge management of the B2C online reviews. The dissertation help B2C companies select incentive strategy for online reviews. Meanwhile,extraction and classification method can help companies better manage online reviews. Customer knowledge management system that based on SOA provides an important reference to enterprises.
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