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Abstract: Smart contracts are one of the three major characteristics of blockchain, and they are also areas where blockchain has application value and flexibility. In essence, a smart contract is a piece of code implemented in a specific scripting language, which inevitably has the risk of security vulnerabilities. How to accurately and timely detect the vulnerabilities of various smart contracts has become the focus and hot spot of blockchain security research. To detect vulnerabilities in smart contracts, researchers have proposed various analysis methods, including symbolic execution, formal verification and fuzzing. With the rapid development of artificial intelligence technology, more and more deep learning-based methods have been proposed and have achieved good results in several research areas. At present, deep learning-based smart contract vulnerability detection methods have not been investigated and analyzed in detail. This paper first briefly introduces the concept of smart contracts and security events related to smart contract vulnerabilities, then introduces the commonly used smart contract features in deep learning-based methods, and describes the deep learning models commonly used in smart contract vulnerability detection. In addition, in order to further promote the research of deep learning-based smart contract vulnerability detection methods, the recent deep learning-based smart contract vulnerability detection methods are summarized and classified according to their feature extraction forms, and are analyzed and introduced from three perspectives: text processing, static analysis and image processing. Finally, the challenges and future research directions in this field are summarized.
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