【摘要】 对搜索引擎返回的结果进行初步判定有利于优化语义搜索过程，提高搜索的准确性和效率。谷歌搜索引擎在所有的搜索引擎中占据主导地位，然而其返回的结果往往非常复杂，目前并没有有效的方法能够对搜索页面的结果做出准确的判断。针对以上问题，该文从数据特征和模型结构设计出发，制作了一个适用于谷歌搜索结果判定的数据集，接着基于预训练模型设计了一种双通道模型(DCFE)用于实现对谷歌搜索结果的判定。该文提出的模型在自建数据集上的准确率可以达到85.74%,相较于已有的模型拥有更高的精度。更多还原

【Abstract】 The preliminary judgment of the results returned by the search engine is of substantial significance to optimizing the search process. As a dominant search engine, Google often returns very complex results, for which there is no effective way to make accurate judgments on the results of search pages. This paper first constructs a data set suitable for Google search result classification, and then, proposes a dual-channel model(DCFE) based on the pre-training model to determine the Google search results. The accuracy of our model on the self-built dataset reach 85.74%, which has higher accuracy the existing models.更多还原