Abstract:  There is a phenomenon of domain drift in webpage search results, where the returned webpage is not related to the search keyword domain, resulting in that users are unable to search for demand information. Therefore, a web search sorting algorithm based on Softmax regression classification model is proposed. Through the Feature selection of web search text, the corresponding feature items are obtained. Using the vector representation model, the selected web search text feature items are converted into formatted data, and the web search text data is balanced to obtain the web search text data set. Using the Softmax regression classification model, the web search text dataset is classified and processed, the types of web search texts is predicted. And the OkapiBM25 algorithm is used to sort web search texts, achieving web search sorting. The experimental results show that the proposed algorithm performs well in web search sorting, effectively improving the accuracy of web search sorting and avoiding domain drift during the process of web search sorting.

Key words: softmax regression classification model, sort web search, text preprocessing, term-frequency-inverse document frequency(TF-IDF) algorithm, OkapiBM25 algorithm