In e-commerce websites, web mining web page recommendation technology has been widely used. However, recommendation solutions often cannot meet the actual application needs of online shopping users. To address this problem, this paper proposes an e-commerce web page recommendation solution that combines semantic web mining and BP neural networks. First, the web logs of user searches are processed, and 5 features are extracted: content priority, time consumption priority, online shopping users' explicit/implicit feedback on the website, recommendation semantics and input deviation amount. Then, these features are used as input features of the BP neural network to classify and identify the priority of the final output web page. Finally, the web pages are sorted according to priority and recommended to users. This project uses book sales webpages as samples for experiments. The results show that this solution can quickly and accurately identify the webpages required by users.

翻译：在电子商务网站中，网页挖掘与网页推荐技术已得到广泛应用。然而，现有推荐方案往往难以满足在线购物用户的实际应用需求。为解决这一问题，本文提出一种融合语义网页挖掘与BP神经网络的电子商务网页推荐方案。首先，对用户搜索的网页日志进行处理，提取内容优先级、耗时优先级、在线购物用户对网站的显式/隐式反馈、推荐语义及输入偏差量这5项特征。随后，将这些特征作为BP神经网络的输入特征，对最终输出网页的优先级进行分类识别。最后，依据优先级对网页进行排序并向用户推荐。本项目以图书销售网页为样本进行实验，结果表明该方案能够快速准确地识别用户所需网页。