This paper presents a strategic and modern scheme for annotating microblogs, specifically targeting innovation and strategy as a domain of choice. In the era of Web 3.0, the proposed approach integrates semantic intelligence with robust machine learning and deep learning models within a unified architecture. This framework introduces an innovative and strategic knowledge stack, comprising eBooks, glossaries, abstracts, and keywords, which aids in generating high-density, domain-specific auxiliary knowledge for the model. The framework effectively shortlists informative terms for category extraction from microblog datasets, with knowledge enrichment achieved through metadata generation. This metadata is further inter-classified using a powerful RNN classifier. The top 10% of the classes identified by the RNN classifier are subsequently processed through an XGBoost classifier. Additionally, the model formalizes a semantic network and performs semantic similarity computations using Adaptive PMI (APMI) and the Horn’s Index. This optimization enhances the model’s overall precision, accuracy, and F-measure, achieving the lowest error rate, making it a best-in-class framework for knowledge-centric microblog tag.