The study of Semantic textual content class (STC) is regularly a critical component of natural Language Processing (NLP). It looks at what is particularly critical in regions, including sentiment evaluation, information retrieval, and other text understanding obligations. Conventional machine learning models and Support Vector Machines (SVMs) and Naïve Bayes have been used for these tasks. However, recent research has explored Deep notion Networks (DBNs) in this regard. This paper affords an innovation evaluation of DBNs for STC issues, which mainly seeks to research the overall performance of the Deep notion Networks in evaluating other present techniques. To evaluate the overall performance of DBNs in this venture, we employ a Python-based, totally data-rich benchmark dataset used for sentiment evaluation. Outcomes demonstrate that the accuracy of DBNs may be similar to or higher than SVMs and Naïve Bayes, reaching almost identical overall performance. The results advocate that the DBN technique for STC is a feasible and competitive method and offers a much better and correct algorithm for NLP duties.