Research on the Construction of Knowledge Graph for Textile Foreign Trade Negotiation and the Design and Practice of Personalized Recommendation System

Against the backdrop of the restructuring of the global textile trade pattern and the acceleration of digital transformation, textile foreign trade negotiations face multiple challenges arising from complex product specifications, evolving trade rules, and frequent cross-cultural communication. With the continuous development of intelligent information systems and electromagnetic-enabled digital communication technologies, traditional experience-driven negotiation models can no longer satisfy the demand for efficient and adaptive decision-making. Knowledge graphs, as a core technology for structured knowledge representation and intelligent reasoning, provide an effective approach for integrating multi-source heterogeneous information and supporting personalized decision-making in textile foreign trade negotiations. This study follows the technical framework of “knowledge graph construction–recommendation algorithm optimization–system development–practical verification” to investigate the construction of a knowledge graph and a personalized recommendation system for textile foreign trade negotiations. The proposed framework enhances semantic association analysis and user-oriented recommendation capability while providing a reference for intelligent information interaction and knowledge-driven decision support in data-intensive communication environments.