Everyone may now learn more conveniently by using e-learning platforms to study online. For these systems to offer an initial personalized learning path (PLP), a component must function as a content recommendation system (RS). This component must also be able to continuously modify the path to accommodate the learner's learning characteristics and the available learning materials in real time. The provision of highly tailored suggestions is still beset by problems like cold start concerns and data sparsity. Recently, there has been a lot of interest in RS development based on knowledge graphs (KG). KGs can leverage the properties of users and items within a unified graph structure, utilizing semantic relationships among entities to address these challenges and offer more relevant recommendations than traditional methods. In this paper, we provide a KG-based learning path recommendation system to aid in English language acquisition by producing a series of lessons intended to successfully lead learners from their present proficiency level to their desired level. We created a domain KG architecture that includes important idea classes and their connections, especially for preparing English certification examinations. Next, to develop an initial PLP recommendation (PLPR) model, we investigated and used graph data mining algorithms (GAs). Lastly, we devised a method to modify the original PLP's lesson sequence to accommodate the learners’ learning characteristics following each real-time interval. With the help of our gathered dataset, consistent experimental conditions, and a chosen set of weights, we assessed our solution using standards like accuracy, efficiency, stability, and execution time.