Accurate assessment of pilot cognitive states is critical for ensuring flight safety, yet existing methods exhibit limitations in fusing multimodal physiological signals. To address this, this paper proposes a dual-stream deep learning network based on bidirectional cross-modal attention. The model adopts a parallel dual-branch architecture: The electroencephalography (EEG) branch quantifies brain functional connectivity through phase locking value (PLV) features and employs a densely connected network enhanced with squeeze-and-excitation (SE) modules for deep feature extraction; the electrocardiogram （ECG） branch extracts heart rate variability (HRV) and waveform features, processed by a residual-connected multilayer perceptron to characterize autonomic nervous system activity. Building upon this, an innovatively designed bidirectional cross-modal attention module dynamically weights and fuses the dual-path deep features to achieve precise classification of three states—concentrated attention, distracted attention, and startle/surprise. Experimental results on the NASA public dataset demonstrate an overall recognition accuracy of 97.44%. Ablation and comparative analyses confirm that the fusion strategy significantly outperforms single-modality analysis and simple feature concatenation methods. The study reveals that deep integration of EEG functional connectivity and ECG physiological information via attention mechanisms effectively enhances cognitive state recognition performance. This approach provides reliable technical support for developing objective and efficient pilot state monitoring systems, holding significant application value for improving flight safety.