  The automatic search performance of search engines has become an essential
part of measuring the difference in user experience. An efficient automatic
search system can significantly improve the performance of search engines and
increase user traffic. Hadoop has strong data integration and analysis
capabilities, while R has excellent statistical capabilities in linear
regression. This article will propose a linear regression based on Hadoop and R
to quantify the efficiency of the automatic retrieval system. We use R's
functional properties to transform the user's search results upon linear
correlations. In this way, the final output results have multiple display forms
instead of web page preview interfaces. This article provides feasible
solutions to the drawbacks of current search engine algorithms lacking once or
twice search accuracies and multiple types of search results. We can conduct
personalized regression analysis for user's needs with public datasets and
optimize resources integration for most relevant information.
