Design And Implementation Of Intelligent Search Engine Based On Elastic Search

With the development of society,the pressure of competition between people has gradually increased.With the rapid development of the Internet and the rapid popularization of mobile terminals,how to use fragmented time for learning is also a general trend.Under this trend,there are more and more learning platforms,and the number of courses in the platform has also shown an explosive growth.This causes users to spend a lot of time choosing the courses they need.Although the traditional full-text search engine can provide corresponding search information in this scene,the search information cannot meet the needs of users,and there will be a lot of complicated results integrated into it.Such search results are not user-friendly,how to improve user's search experience is an urgent problem to be solved.This thesis discusses the design and implementation of a search engine framework with high scalability,and implements a simple text processing function,and provides three reordering components.The system uses Python language as the development language,Elastic Search as the basic search engine framework,My SQL as a tool for storing basic data.By summarizing the existing search engine technology,a style search framework based on pipeline filter is proposed.The core architecture of this search is divided into three layers,namely,the search content expansion module,the basic search service module and the reordering module.The author independently designs and completes the development of each module of the whole system.The details of each module are as follows:(1)Search content extension module: Use word segmentation tool to segment the text content of user search,extract the course category and other keyword information,and judge the relationship between keywords.Then use the synonym tool to extend the search keywords and combine all the extension results,and construct the combination results into the data objects that the basic search module can use.(2)Basic search service module: This module is to further encapsulate the external call interface of Python language provided by Elastic Search,complete the automatic assembly of Elastic Search operation structure and the automatic extraction of Elastic Search operation results.(3)Reordering module: This module aims to provide a basic framework for dynamically loading instances of reordering algorithms,and provide users with a mechanism for selecting and calling reordering algorithms.Three reordering algorithms based on intelligent recommendation results,curriculum similarity and curriculum system are implemented.The corresponding reordering algorithm is obtained by using the multi-layer perceptron model and user-related data training,and the reordering component recommendation service is provided for users.This thesis analyzes the requirements of the above functional modules,and introduces the design and implementation process in detail.Finally,functional and non-functional tests are carried out to ensure that the system can provide a better service for the users of smart finance learning platform.