A Smart Contract Classification Method Based on Label Embedding and Collaborative Attention Mechanism

Abstract

In the current task of text classification for smart contracts, the role of labels in the final classification performance is relatively small, and label information has not been better utilized. In order to make effective use of label information, this paper proposes a smart contract classification method based on label embedding and collaborative attention mechanism. This method introduces a collaborative attention mechanism to generate semantic representations of smart contracts related to labels and smart contract semantic vectors. This approach enables the classification model to focus on the relevant parts of both, thereby effectively improving the accuracy of classification.