Since the development of Blockchain 2.0, the smart contract has become the core of blockchain. However, smart contracts with inaccurate or non-standard codes and settings may cause security vulnerabilities, extra expense cost and wast of computing resource. To avoid these problems and assist users to create new smart contract or apply existing smart contract in a more efficient way, we propose smart contract recommendation by regarding smart contract as a special form of software service in a blockchain system. First, four real-world datasets are obtained from Ethereum and EOSIO for smart contract recommendation. Then, a novel smart contract recommendation framework is proposed and evaluated. In the large-scale experiments, the results validate the feasibility of smart contract recommendation. Additionally, the datasets are publicly released online to other researchers for further studies on smart contract recommendation.

Smart contracts can be considered as a service in the blockchain system and have been applied in many fields, covering financial products, online games, real estate, transportation and logistics. However, smart contract technology is still in its infancy. Development task is facing many difficulties and challenges, thus providing a set of new or improved development aids for the smart contract ecosystem is an urgent problem that needs to be solved. This paper proposes a smart contract code recommendation method based on graph neural network, which aims to facilitate the development of smart contracts and help developers realize smart contracts faster and more securely. Experimental results show that this method is better than the existing model of smart contract code recommendation in terms of accuracy.