In response to the high cost of manual design and lack of automation and intelligence in the current field of brand identity design, this article combines multimodal image processing technology to apply a ViT (Vision Transformer) neural network model for improvement. Brand identity is detected and recognized in online and offline channels, and features are extracted from multimodal image data using neural network models. Existing designs are optimized, and neural network models are trained. The neural network model is adjusted and optimized using multimodal image processing technology to automatically detect the application effect of brand identification on various media platforms. The accuracy of the model is significantly improved in multiple training tests, indicating a significant improvement in its performance. Finally, the consistent application of brand identification on various media and platforms is also achieved.