Visual communication design of web-based learning resources in the digital era.

The article focuses on a deep learning-based approach to visual communication design for web learning resources, aiming to enhance user experience and learning effectiveness. It integrates convolutional neural networks (CNN) for automatic interface design, a recommendation algorithm combining implicit semantic modeling for personalized content suggestions, and machine translation using recurrent neural networks (RNN) and Transformer models for language description translation. Performance evaluations demonstrate that this method achieves faster design speed, higher F1 scores, and better recommendation accuracy compared to existing models, producing a greater number of interface solutions and improving learner satisfaction by up to 14%. The study notes a limitation in browser kernel compatibility, suggesting future work to extend the framework’s applicability across multiple browser environments.