Visual neural decoding based on electroencephalography(EEG) is designed to retrieve images perceived by subjects from brain activity. To address the issues of low signal-to-noise ratio in EEG signals, redundancy in visual teacher features and the difficulty for a single teacher to simultaneously preserve both semantic structure and perceptual details, a compression-guided hyperbolic interpolation fusion method for visual neural decoding (CHIF) is proposed in this paper. First, lightweight compression modules are introduced into the structure-enhancement branch and the appearance-preservation branch to compress and adapt visual representations. Consequently, the modality gap between high-dimensional visual features and EEG representations is reduced. Second, a hyperbolic interpolation fusion module is designed. Teacher features are mapped into hyperbolic space. A unified teacher target is constructed through geodesic interpolation. Cross-modal alignment is achieved by combining hyperbolic distance and bidirectional contrastive learning. The experiments on an EEG-image retrieval dataset of 10 subjects demonstrate that CHIF achieves effective retrieval performance.