This paper presents a multi‐mode fusion human‐computer interface integrating Electroencephalogram (EEG) and Electrooculogram (EOG) signals to enhance interaction speed and accuracy. Traditional Steady‐State Visual Evoked Potential (SSVEP)‐based Brain‐Computer Interface (BCI) systems suffer from low refresh rates in liquid crystal displays (LCDs), reducing signal quality and decoding performance. To address this, a partitioned backlight LCD with 384 zones is employed to generate high‐refresh‐rate visual stimuli, improving SSVEP signal induction. EOG signals are simultaneously captured to monitor gaze direction and eye movements, enabling real‐time intention decoding. A deep learning algorithm classifies fused EEG‐EOG data, while canonical correlation analysis (CCA) with sliding window processing ensures prompt response upon EOG detection. Experimental results demonstrate enhanced accuracy, reliability, and noise robustness in complex environments. The proposed system holds potential for applications in intelligent driving, smart homes, and medical rehabilitation.