SEARCHX: An Integrated Framework of Distributed Intelligent Search Services Based on Web Browser

To address the growing demand for web search and improve the performance and accuracy of search systems, this study proposes a distributed intelligent search service integration framework based on SEARCHX. This framework leverages the local computational power of the browser, integrating inverted indexing, data sharding, and replication mechanisms, as well as the Term Frequency-Inverse Document Frequency (TF-IDF) intelligent ranking algorithm. These components enable front-end distributed processing of search tasks and multi-source result fusion. Experiments are conducted on six major browser platforms, Chrome, Firefox, Edge, Safari, etc., using the open-source Text REtrieval Conference (TREC) dataset. The system’s response performance and accuracy are evaluated under varying search loads. The experimental results show that, compared to the unoptimized version, the optimized SEARCHX reduces the average response time by approximately 27 per cent under medium-to-high load conditions. Precision improves by an average of 0.05, and the F1 score increased by more than 0.04 on all platforms. The system also demonstrates good stability and consistency across multiple platforms. SEARCHX provides a viable approach to building decentralized, high-efficiency, and easily deployable intelligent search services, with strong practical value and expansion potential. This study aims to construct a decentralized, cross-platform, and high-performance intelligent search service framework, offering a more efficient, stable, and accurate technical support solution for users in complex search environments.