There are multiple distinct sources through which numerous news contents that occur in digital medium tend to converge. Web contents constitute massive number of features. Complete coverage of all kinds of news is absolutely vital to retain customer confidence and to have a competitive edge over other news agencies. Aggregating such massive news content from different heterogeneous sources requires an integration of convergent computing. Classification of these online news is a challenging task in the age of Internet where news keeps flowing from several heterogeneous sources. Due to constant rise in manipulation of web contents, accurate classification of digital news is the need of the hour. Precise detection of specific news into their respective class is a major challenge in recent times. In this scenario, the need of an automated predictive-based approach can be of great use in effective organization and classification of news in a pool of web portals. This research study comprises the application of Bernoulli model to determine the effectiveness of multi-class digital news categorization that arrive in real time. The system model presented in this analysis was evaluated using python, and the result was demonstrated using six distinct classes of news with a 6000 feature size dataset from TagMyNews dataset. The classification accuracy using the Bernoulli model was computed to be 98.4%, while the evaluated precision metric was 92.7%, and recall value was 90.6%. The F-Score metric generated an optimum value of 91.4%. The execution time for Bernoulli model was only 12 s. The computed result using Bernoulli model was compared with some other related renowned existing works and the results generated by Bernoulli model gave optimum performance and the news classification efficiency is highly enhanced.