Analytic prediction for acceptable pricing in industry interaction with complex network evolution based on knowledge graph fusion

This paper proposes a trend prediction analysis method based on the evolution of knowledge graph fusion for the analysis of price fluctuation trends with limited information and lacking historical data. Based on an actual business case, the article first establishes a knowledge graph and corresponding data model based on the price analysis and application scenarios in business decision-making and based on the collected data. Afterward, the evolution mechanism model and the corresponding theoretical basis are described. After that, the feasibility of the proposed method is proved theoretically and via simulation verification. This method does not require tags and historical data as necessary support, thereby reducing the data requirements of machine learning predictions, and can be used as a supplementary solution to complete trend analysis with less requirement on data curation. The method's stability and feasibility have been substantiated through experiments, showcasing distinct effects on the trend emanating from different interrelated knowledge graphs. Recognizing the potentially conflicting constraints between diverse correlations, we undertake a comprehensive evaluation of each map's influence. This evaluation is achieved through knowledge graph fusion processing, enabling a predictive assessment under the collective impact of various factors.