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17 reviewsIntegrating multimodal semantic features, such as images and text, to enhance visual neural representationshas proven to be an effective strategy in brain visual decoding. However, previous studies have either focusedsolely on unimodal enhancement techniques or have inadequately addressed the alignment ambiguity betweendifferent modalities, leading to an underutilization of the complementary benefits of multimodal features or areduction in the semantic richness of the resulting neural representations. To address these limitations, we propose a Multimodal Fusion Alignment Neural Representation Model (MFA-NRM), which enhances visual neuraldecoding by integrating multimodal semantic features from images and text. The MFA-NRM incorporates a fusionmodule that utilizes a Variational Autoencoder (VAE) and a self-attention mechanism to integrate multimodalfeatures into a unified latent space, thereby facilitating robust semantic alignment with neural activity. Additionally, we introduce prompt techniques that adapt neural representations to individual differences, improvingcross-subject generalization. Our approach also leverages the semantic knowledge from ten large pre-trainedmodels to further enhance performance. Experimental results on the Natural Scenes Dataset (NSD) show that,compared to unimodal alignment methods, our method improves recognition tasks by 18.8 % and classificationtasks by 4.30 %, compared to other multimodal alignment methods without the fusion module, our approachimproves recognition tasks by 33.59 % and classification tasks by 4.26 %. These findings indicate that the MFANRM effectively resolves the problem of alignment ambiguity and enables richer semantic extraction from brainresponses to multimodal visual stimuli, offering new perspectives for visual neural decoding.