Development of a Movie AI Question Answering System Based on Knowledge Graph

With the increasing influence of the internet, information retrieval technology needs to be continuously upgraded. The development of the internet drives the transformation of the film industry, with an increase in the number of online movies, a flourishing of professional film websites and forums, and an increasing influence of online comments. There are still shortcomings in the relationship between the internet and film evaluation, and further exploration is needed. The development of this system adopts a front-end and back-end separation mode, with Vue technology architecture used in the front-end and Django framework used in the back-end. The knowledge graph can organize and express movie information, providing data support for intelligent question answering and comment systems. The system constructs a graph by collecting movie data, achieving storage, management, and querying, providing intelligent and personalized services, and designing movie review functions to provide comprehensive information and evaluation references; The question answering function is implemented through natural language processing technology (NLP) and graph database technology, which can recognize the user’s query intention and retrieve relevant information, generate accurate and concise answers. Its core module is the movie AI question answering function based on Neo4j. After the user raises a question, the comment system will parse the question and construct a Cypher query statement. After execution, the result will be returned and displayed to the user, achieving intelligent answers and personalized services. In system design, the connection between the movie AI question answering module and the Neo4j database is crucial. This connection enables effective communication and information exchange between user questions and movie knowledge graphs, providing users with convenient and accurate movie related information. Through deep integration with the Neo4j database, the system can better understand user needs and make corresponding intelligent responses, improving user experience and system practicality. The development of this system brings certain application reference value to similar movie question answering systems.