There is a dearth for a strategic automatic framework for ontology synthesis and generation which encompasses semantics oriented reasoning for a domain of importance such as microeconomics. This research paper proposes ontology synthesis and generation model that encompasses hybrid learning paradigms for microeconomics as a prospective domain. The dataset of documents relevant to microeconomics as a domain is subjected to classification using the LSTM classifier and models such as TF-IDF, Structural Topic Modeling are encompassed to generate the initial seed knowledge from the dataset of documents. Standard strategic knowledge store repositories such as YAGO and RDF generation helps in adding to the auxiliary knowledge of the model. The exponential increase of the knowledge is achieved through metadata generation and making it more atomic into the model through the classification by the convolutional neural networks (CNN). Semantics oriented reasoning is achieved through standard strategic semantic similarity measures such as NPMI, Associate PMI, Second-Order co-occurrence PMI, Jaccard Similarity and intermediate optimization of the results is achieved using particle swarm optimisation (PSO) to be the best in class strategic entities as an ontology. The proposed framework is the first in class framework that formalizes a ontology for microeconomics as a domain and achieves a precision of 95.09% with a F-measure of 96.957% and an FDR value of 0.05, thus proving to be the best in class model for ontology generation.