Decision Tree Algorithm Based Legal Liability Determination and Contract Fulfillment Path in the Execution of Smart Contracts

The ontological issues such as the concept, features, and attributes of smart contracts written in code and running on the blockchain have been the focus of research in the academic community.In this paper, we first construct a smart contract illegal behavior determination model based on the C4.5 decision tree algorithm, which realizes accurate prediction and determination of illegal behaviors existing in smart contract transactions by extracting multiple attribute features of smart contract transaction data.Then, the correlation between smart contract features and contract risk is analyzed by Pearson coefficient, and the risk assessment evaluation system of smart contract performance is constructed by using hierarchical analysis.Finally, the fulfillment path of smart contract is proposed by synthesizing all the analysis results.Among the 24 randomly selected samples, the total prediction probability of the illegal behavior determination model based on the C4.5 decision tree algorithm reaches 95.83%, which is able to effectively identify the illegal behavior of smart contracts.The Pearson chi-square value between smart contract features and contract risk is 224.6317, and the Sig.(two-tailed) value is 0.000, indicating that there is a significant correlation between the two.By constructing a smart contract risk assessment index system, this paper designs a dynamic monitoring model of smart contract fulfillment risk level, and proposes a smart contract fulfillment path from the aspects of reasonable allocation of legal responsibility and legal regulation of contract fulfillment.