The current situation of automated information processing systems and the problems existing in semantic coherence maintenance across multi-domain environments are proposed to achieve higher processing accuracy, improved contextual understanding, and enhanced system efficiency. This paper aimed at opening new pathways in algorithmic advancements that affect semantic integrity and coherence in multi-domain natural language processing (NLP) tasks and putting forward corresponding quantitative models and computational frameworks. To meet the increased semantic analysis demands of scalable and secure NLP applications, the capability of deep learning-based coherence optimization must improve. One important technique for doing this is to redesign algorithmic structures, the logical processing flows that NLP models follow to perform context-aware text processing. In this study, the graph-based hypernetwork method in semantic coherence measurement combines the Transformer-based learning method based on contextual embeddings, topological representations, and coherence score optimization. Based on the innovative design method of multi-layered hypergraphs, the extracted semantic dependencies are used to build a more accurate coherence assessment model through deep reinforcement learning with self-attentive training method. The research results show that the constructed model can systematically analyze cross-domain textual coherence, and semantic consistency is more robust, more adaptive, and more scalable to dynamic linguistic variations. Analyzing this trend reveals that deep learning-driven coherence modeling has been the largest breakthrough to NLP advancements, with self-supervised learning techniques and adaptive re-ranking mechanisms (e.g., context-sensitive embeddings) leading the way, but that rule-based coherence assessment has faded in recent decades.