Research on Multi-Dimensional Dynamic Financial Analysis System Based on GNN-Transformer Integrated Model

随着金融市场数据规模、复杂度和动态性的显著增加，传统金融分析方法在准确性和效率方面遇到瓶颈。为此，本研究提出了一种基于图神经网络(GNN)与Transformer集成的多维动态金融分析系统。通过将GNN的图结构分析能力和Transformer的自注意力机制有效融合，系统能够同时处理和分析资产间复杂的交互关系及金融时间序列的长期依赖性。实验结果表明，本方法在股票市场趋势预测、实时市场情绪分析以及交易策略优化等方面表现出了明显优于传统模型的优势。论文进一步探讨了集成模型在金融风险预测、市场情绪分析及高频交易算法优化中的实际应用效果。With the significant increase in the scale, complexity and dynamics of financial market data, traditional financial analysis methods pose challenges in terms of accuracy and efficiency. To this end, this study proposes a multi-dimensional dynamic financial analysis system based on Graph Neural Network (GNN) and Transformer. The system effectively combines the graph structure analysis of GNN and the self-attention mechanism of Transformer to simultaneously handle and analyze the complex interactions between assets and the long-term dependencies of financial time series. The results of experiments show that this method outperforms traditional models in stock market trend prediction, real-time market sentiment analysis and trading strategy optimization. The paper also explores the practical application effects of this integrated model in financial risk prediction, market sentiment analysis and high-frequency trading algorithm optimization.