Dynamic Feature Fusion: Combining Global Graph Structures and Local Semantics for Blockchain Fraud Detection

--The advent of blockchain technology has facilitated the widespread adoption of smart contracts in the financial sector . However, current fraud detection methodologies exhibit limitations in capturing both global structural patterns within transaction networks and local semantic relationships embedded in transaction data. Most existing models focus on either structural information or semantic features individually, leading to suboptimal performance in detecting complex fraud patterns.In this paper, we propose a dynamic feature fusion model that combines graph-based representation learning and semantic feature extraction for blockchain fraud detection. Specifically, we construct global graph representations to model account relationships and extract local contextual features from transaction data. A dynamic multimodal fusion mechanism is introduced to adaptively integrate these features, enabling the model to capture both structural and semantic fraud patterns effectively. We further develop a comprehensive data processing pipeline, including graph construction, temporal feature enhancement, and text preprocessing. Experimental results on large-scale real-world blockchain datasets demonstrate that our method outperforms existing benchmarks across accuracy, F1 score, and recall metrics. This work highlights the importance of integrating structural relationships and semantic similarities for robust fraud detection and offers a scalable solution for securing blockchain systems. LOCKCHAIN technology has developed rapidly in recent years and has triggered far-reaching changes in several fields, especially in the financial industry [1]. However, as the popularity of blockchain applications grows, so does the significant increase in fraudulent behaviors it has brought about, with serious implications for society [2]. Blockchain technology, due to its decentralization and transparency, has become a tool for unscrupulous individuals to exploit, although it provides greater security and efficiency in financial transactions [3]. For example, the application of blockchain technology in the supply chain is seen as an effective means to enhance transparency and traceability, but it also faces a crisis of social trust due to fraudulent behavior [4].