Abstract: With the rapid development of AI technologies in the aviation industry,understanding and quantifying human-machine trust have become especially important for flight safety,handling emergencies,and improving flight efficiency.Research has shown that physiological and behavioral features are closely related to trust,but current trust prediction studies seldom consider these features.To address this gap,this research proposes a deep learning-based human-machine trust prediction model,which integrates TCN and GAT,using gaze and behavioral features as inputs.To more effectively capture the complex relationships between trust states and their influencing factors(such as system performance and various behavioral features),a causal graph mo-del is constructed.The study conducts experiments based on the MATB task and collects trust-related data from 13 participants.Data analysis results indicate that the experimental design is sound and successfully identifies features that are highly correlated with trust,which are used for trust prediction.The experimental results demonstrate that,compared to existing trust models and traditional methods,the proposed model improves prediction accuracy by at least 16%.This result not only validates the effectiveness of the proposed approach but also highlights the significant potential of combining deep learning techniques with trust-rela-ted features for prediction tasks.Furthermore,since the model is not limited to a specific domain,this research can provide valuable references for trust prediction in other fields.

Key words: Trust prediction, Behavioral features, Deep learning