Precise Query Strategy of Intelligent Search Engine Based on Semantic Analysis Computer Algorithm

With the rapid advancement of Internet technology, the explosive growth of information has made the role of search engines in providing accurate and relevant search results increasingly critical, which is directly related to the efficiency of information acquisition and the user experience. However, traditional search engines often find it difficult to accurately meet the user’s query needs when faced with massive and changing network information. This paper systematically explores the application of precise query strategies based on natural language processing and search result ranking algorithms in intelligent search engines, proposes precise query strategies that combine semantic understanding, context analysis and personalized recommendation, and constructs corresponding algorithm models. Through experimental verification, this paper evaluates the effect of the proposed strategy in actual search applications, demonstrating its advantage of significantly improving search accuracy (about 28%), and also points out the space for optimization in specific complex query scenarios. The experimental data strongly proves that compared with traditional search engines, the intelligent search engine using the strategy in this paper can show better search performance in various query scenarios, effectively improving the efficiency of information acquisition and the user experience.