This research works on creating a hybrid Knowledge Recommendation System (KRS) for an Entrepreneurship Course using the Knowledge Graph (KG) and Clustering Technologies (CTs). The system aims at improving students' learning experience by providing relevant learning materials and even focusing on learner preferences. These results are already part of the student profiles and advanced learning paths modules that aim to adapt to a certain student's learning style, tastes, and level of mastery of the subject. Additionally, the system creates feedback paths to hammer the recommendation algorithm in constant improvement and evolution as well. Upon the conclusion of tests and discussion, the effectiveness and utility factors put in place in our proposed KRS are assured. Moreover, the quick and efficient responsiveness of the system to adapt to students' changing needs and peculiar demands for individual learning support further improves learning outcomes and the satisfaction level of the course participants.