The evaluation of smart contract reputability is essential to foster trust in decentralized ecosystems. However, existing methods that rely solely on code analysis or transactional data, offer limited insight into evolving trustworthiness. We propose a multimodal data fusion framework that integrates code features with transactional data to enhance reputability prediction. Our framework initially focuses on AI-based code analysis, utilizing GAN-augmented opcode embeddings to address class imbalance, achieving 97.67% accuracy and a recall of 0.942 in detecting illicit contracts, surpassing traditional oversampling methods. This forms the crux of a reputability-centric fusion strategy, where combining code and transactional data improves recall by 7.25% over single-source models, demonstrating robust performance across validation sets. By providing a holistic view of smart contract behaviour, our approach enhances the model's ability to assess reputability, identify fraudulent activities, and predict anomalous patterns. These capabilities contribute to more accurate reputability assessments, proactive risk mitigation, and enhanced blockchain security.

翻译：智能合约信誉度评估对于培育去中心化生态系统中的信任至关重要。然而，现有方法仅依赖代码分析或交易数据，对动态演化的可信度提供有限洞察。本文提出一种多模态数据融合框架，通过整合代码特征与交易数据以增强信誉度预测能力。该框架首先聚焦于基于人工智能的代码分析，利用GAN增强的操作码嵌入技术解决类别不平衡问题，在检测非法合约方面达到97.67%的准确率与0.942的召回率，其性能超越传统过采样方法。这构成了以信誉度为核心的数据融合策略的关键基础，实验表明代码与交易数据的融合使召回率较单源模型提升7.25%，并在多个验证集上展现出鲁棒性能。通过提供智能合约行为的全景视图，本方法增强了模型评估信誉度、识别欺诈活动及预测异常模式的能力。这些能力有助于实现更精准的信誉度评估、主动风险缓解以及增强的区块链安全性。