Intelligent Editing and Publishing System Based on Deep Learning and Multi-Dimensional Cross-Encoding

Abstract: With the rapid development of intelligent technologies in recent years, to enhance the level of intelligence in semantic modeling and automated processing of editing and publishing systems, this paper designs an intelligent editing and publishing system based on deep learning and multidimensional cross-encoding. The system's multidimensional cross-encoder performs semantic modeling of user-input keywords and achieves precise control over topics in the editing process through a weighted concatenation attention mechanism. To further optimize system performance, this study introduces a Generative Adversarial Network (GAN) to optimize the semantic representation of the generator. Experimental validation shows that the system performs excellently on evaluation metrics such as BLEU-3, BLEU-4, and Correlation, particularly with significant improvements in the consistency between keywords and text semantics. The research results indicate that the application of this system in intelligent editing and publishing processes offers significant technical advantages, effectively supporting topic planning and content proofreading in intelligent publishing workflows and providing technical support for the development of the intelligent publishing field.