近年來傳統線上購物正面臨著一連串的市場困境，如削價競爭、廉價品競爭等，因此導致銷售量之成長趨緩，反觀跨境線上購物卻出現了蓬勃發展的態勢，因而讓跨境線上購物成為驅動經濟活動與國際貿易的新引擎。另一方面，由於跨境線上購物的情境複雜性遠高於傳統的境內線上購物，業者們欲開發一海外新市場，必須先了解該地消費者行為與其購買決策過程後，才能制定出好的商業策略，並且進一步將產品導向的服務轉化成為以顧客導向的服務，才有機會為傳統線上購物之困境另闢生機。因此，引取並了解消費者所體認的內在價值是經營跨境線上購物最重要的成功因素。 本研究將試圖將傳統境內線上購物研究擴展到跨境線上購物議題，藉由文字探勘(Text Mining)分析、語意情感分析與 k-means 分群演算法，挖掘出消費者對於所購買商品之評論的常見內容型態與所購買商品之類別，並試圖找出跨境網購平台上各項因素及商品評論對於產品銷售量間之關連性，提供未來研究者及跨境網購平台業者決策之依據。 / While online shopping websites are facing the difficulties of price and low-quality competition, cross-border online shopping is on a vigorous development trend, showing that cross-border online shopping is an important trend of online shopping field. Due to the complexity of cross-border online shopping is much higher than the traditional domestic online shopping, so understanding the value of cross-border online shopping consumers is the most important success factors. Companies want to develop new markets abroad, must understand the local consumer’s behaviour and their decision-making process in order to make good business strategies. This study uses text mining analytic technology, semantic analysis techniques, and k-means clustering algorithm to identify characteristics of consumers’ reviews and the common categories of goods they purchased. After getting the reason why consumers use cross-border online shopping service and what values they got in this service. Researcher can predict and analyse the evolution and development of cross-border online shopping, provide reference for future online shopping academic studies and online shopping industry’s decision-making.