Research on massive information query and intelligent analysis method in a complex large-scale system

With the rapid growth of big data and network information, it is particularly important to perform information query and intelligent analysis on unstructured massive data in large-scale complex systems. The existing methods of directly collating, sorting, summarizing, and storing retrieval of documents cannot meet the needs of information management and rapid retrieval of massive data. This paper takes the standardized storage, effective extraction and standardized database construction of massive resume information in social large-scale complex systems as an example, and proposes a massive information query and intelligent analysis method. The method utilizes the semi-structured features of the resume document, constructs the extraction rule model of various resume data to extract the massive resume information. On the basis of HBase distributed storage, with the help of parallel computing technology to optimize the storage and query efficiency, which ensures the intelligent analysis and retrieval of massive resume information. The experimental results show that this method not only greatly improves the extraction accuracy and recall rate of resume information data, but also compared with the traditional methods, there are obvious improvements in the three aspects of massive information retrieval methods, query usage efficiency, and the intelligent analysis of complex systems.