A strategic model for proposing web pages and knowledge-focused paradigms that tackle the highly coherent structure of Web 3.0 is the need of the hour and the primary requirement. This paper presents a framework that encompasses strategic knowledge which is obtained incrementally from the dataset where categories are extracted and subjected to topic modelling, metadata generation and classification. Subsequently, the model also encompasses classification using community-contributed and verified knowledge instances as features. The dataset classification takes place using a lightweight machine learning decision tree classifier. The framework also possesses semantic inclusivity and quantitative semantic analysis using SimRank and online measures at different stages in the pipeline, optimization using particle swarm optimization and latent semantic indexing at different instances in the framework encompasses a good degree of topic modelling into the framework The Simpson’s diversity index is used for selection and the semen is used to classify the metadata which is a highly specialized classification framework. A Precision of 95.54%, recall of 96.89%, F-measure of and 96.21% accuracy of 96.21% have been attained with the suggested framework making it the best model for webpage recommendation.