Given the surge in digital communication, the spread of fraud messages across various communication platforms poses an increasingly significant threat to the public due to their accessibility and ubiquity. Existing fraud detection methodologies predominantly focus on recognized fraud tactics, thus diminishing their effectiveness against unfamiliar or evolving deceptive strategies, a challenge known as domain adaptation. To address this issue, we introduce a novel approach, Graph-Based Syntactic Structure Generation, capable of autonomously learning common syntactic structures within fraud messages without the need for predefined fraud patterns. This method leverages co-occurrence graphs and graph auto-encoder techniques to extract connection state features of each token from extensive texts, thereby acquiring global information essential for identifying complex syntactic structures indicative of fraud messages. Further, we employ an attention-based model for syntactic structure transformation of texts, converting each token into word, part of speech (POS), or # (fixed valued) to facilitate effective syntactic pattern recognition. This approach not only enhances the model’s understanding of textual syntactic structures but also enriches semantic information for subsequent large language model analysis. Experiments conducted on datasets sourced from Cofacts and the 165 anti-fraud platform demonstrate our method’s robust recognition performance on cross-domain datasets, even with limited training data, surpassing traditional methods’ limitations when confronting newly emerged fraud tactics.