Abstract

In the face of the current epidemic situation, news reports are facing the problem of higher accuracy. The speed and accuracy of public emergency news depends on the accuracy of web page links and tags clustering. An improved web page clustering method based on the combination of topic clustering and structure clustering is proposed in this paper. The algorithm takes the result of web page structure clustering as the weight factor. Combined with the web content clustering by K-means algorithm, the basic content that meets the conditions is selected. Through the improved translator of clustering algorithm, it is translated into Chinese and compared with the target content to analyze the similarity. It realized the translation aim of new crown virus epidemic related news report of Japanese Linguistics based on page link mining.

中文翻译：

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基于页面链接挖掘的与日语语言学的COVID-19相关的新闻报道的翻译

摘要

面对当前的流行情况，新闻报道面临着更高准确性的问题。公共紧急事件新闻的速度和准确性取决于网页链接和标签聚类的准确性。提出了一种基于主题聚类和结构聚类相结合的网页聚类方法。该算法将网页结构聚类的结果作为权重因子。结合基于K-means算法的Web内容聚类，选择满足条件的基本内容。通过改进的聚类算法翻译器，将其翻译成中文，并与目标内容进行比较以分析相似性。基于页面链接挖掘，实现了日本语言学新冠状病毒流行病相关新闻报道的翻译目标。