Construction and Application of Robo-Advisory System Based on Knowledge Graph

This study constructs an intelligent investment advisor system based on a knowledge graph to address the complex decision-making needs in financial markets. By integrating natural language processing (NLP) technology and a graph database, the system effectively extracts and analyzes entities and relationships in the investment domain. Experimental results demonstrate that personalized optimization strategies significantly improve the accuracy and response speed of investment recommendations.