To avoid eye image disappearance and inaccurate head pose estimation during image capture, a non-contact method for acquiring eye information is employed to collect facial images, determining the pilot’s current gaze direction from a single image frame. Concurrently, considering the poor classification of current networks due to the neglect of visual obstruction caused by head movements, with a combination of facial images and head poses, a multimodal data fusion network for the pilot’s gaze region classification is proposed using an improved MobileVit model. Firstly, a multi-modal data fusion module is introduced to address the problem of overfitting resulting from size imbalances during feature concatenation. Additionally, an inverse residual block based on a parallel branch SE mechanism is proposed to fully leverage spatial and channel feature information in the shallow layers of the network. Moreover, multi-scale features are captured by integrating the global attention mechanism from the Transformer. Finally, the Mobile Block structure is redesigned and the depthwise separable convolution is utilized to reduce model complexity. Experimental comparisons with mainstream baseline models are conducted using a self-made dataset FlyGaze. The results demonstrate that the PilotT model achieves classification accuracies exceeding 92% for gaze regions 0, 3, 4, and 5, with robust adaptability to facial deflection. These findings hold practical significance for enhancing flight training quality and facilitating pilot intention recognition and fatigue assessment.