Research on Intelligent Generation Method of Test Questions in Communication Operation and Maintenance Field Based on Knowledge Enhancement

With the acceleration of technological iteration, the intelligent transformation of communication operation and maintenance urgently requires supporting talent assessment tools. Currently, traditional test question generation methods rely on static knowledge bases and manual writing. This article proposes an intelligent test question generation method based on knowledge enhancement to address the problems of knowledge update lag and insufficient scene generalization in traditional test question generation methods. By integrating technologies such as Large Language Model (LLM) and Retrieval-Augmented Generation (RAG), a professional vector knowledge base in the field of communication operation and maintenance is constructed. The RAG process is optimized to suppress the illusion of LLM and achieve intelligent generation of operation and maintenance test questions. This provides an efficient and customizable intelligent solution for evaluating the abilities of communication operation and maintenance talents.