Multimodal deep learning for cross-cultural business communication intent understanding and pragmatic failure prediction: a knowledge-graph-enhanced approach

Cross-cultural business communication frequently suffers from pragmatic failures rooted in divergent cultural norms governing speech acts, politeness strategies, and face management. This paper proposes an integrated multimodal deep learning framework that jointly addresses communicative intent recognition and pragmatic failure prediction in cross-cultural business interactions, bridging a gap that existing unimodal and culture-agnostic approaches have left open. The system integrates textual, acoustic, and visual modalities through a hierarchical five-layer architecture comprising modality-specific encoders, cross-modal transformer fusion, and a gated cultural knowledge injection mechanism. A cross-cultural pragmatic knowledge graph encoding speech act conventions, politeness thresholds, and face-negotiation strategies across multiple cultural clusters is constructed and interfaced with neural attention layers via attentive graph readout. A culture-contrastive attention mechanism captures the divergence between culturally distinct pragmatic expectations, providing an interpretable basis for failure prediction. A multi-task joint learning strategy, governed by uncertainty-based dynamic loss balancing, simultaneously optimizes intent classification, failure type prediction, and severity regression. Experiments on a newly constructed multilingual business dialogue dataset spanning six cultural pairings show that the framework achieves 82.8% intent classification F1 and 71.6% failure prediction F1, outperforming unimodal and culture-agnostic baselines by substantial margins. Ablation analyses reveal that knowledge graph enhancement disproportionately benefits culturally distant communication pairs and that non-verbal modalities play a larger role in failure detection than in intent recognition.