The recent advancements in the field of smart contract security have primarily focused on enhancing vulnerability detection and scam prevention mechanisms. Researchers are increasingly leveraging innovative techniques such as graph representation learning and knowledge migration frameworks to improve the accuracy and efficiency of detection tools. Notably, the integration of machine learning models with graph structures has shown promise in analyzing transaction patterns and identifying fraudulent activities, addressing scalability issues inherent in traditional methods. Additionally, the development of adaptive fusion modules and data-free knowledge distillation methods has significantly boosted feature extraction and cross-class adaptation capabilities, leading to more robust detection models. These developments collectively aim to fortify the security landscape of blockchain systems, ensuring greater trust and reliability in smart contract interactions.