<p>Paroxysmal kinesigenic dyskinesia is a rare neurological disorder characterized by brief, recurrent motor attacks that significantly impair quality of life. Prior studies have largely relied on unimodal data, which offer partial insights into neural regulation but are constrained by trade-offs between temporal and spatial resolution. To address this limitation, we developed a multimodal recognition and tracing framework integrating electroencephalography and functional magnetic resonance imaging. We propose GTBL-AF, a deep multimodal neural architecture that captures spatial connectivity and temporal dynamics of brain function through graph attention, Transformers, and bidirectional long short-term memory networks, with cross-attention enabling modality-level fusion. GTBL-AF achieved 94.2% classification accuracy in paroxysmal kinesigenic dyskinesia recognition, significantly outperforming unimodal methods. Incorporating dipole-based electroencephalography source localization and phase-locking value connectivity, we observed increased temporal complexity and reorganized functional connections in key cortical regions, including the prefrontal cortex, temporal pole, and parietal association areas. Whole-brain analyses using sample entropy and small-world metrics revealed greater dynamic uncertainty and enhanced small-world properties in paroxysmal kinesigenic dyskinesia patients, indicative of compensatory neural regulation. Furthermore, network-based statistics identified aberrant synchronous connectivity within circuits mediating cognitive control and motor initiation. This study presents a deep EEG–fMRI multimodal fusion framework for PKD and provides evidence of widespread network reorganization. These findings may contribute to a better understanding of PKD pathophysiology and provide a methodological reference for future multimodal-assisted diagnosis and individualized clinical assessment.</p>