The global proliferation of Massive Open Online Courses (MOOCs) has led to an increasing significance of personalised course recommenders. While existing recommendation approaches that combine Graph Neural Networks (GNNs) and knowledge graphs have been effective in enriching feature representations and addressing the personalised needs of learners, GNNs still encounter significant challenges, global information loss caused by long-range dependencies issue when processing large-scale graph data. To address these limitations, this paper proposes, for the first time, a novel MOOC recommendation model that integrates knowledge graph, GNNs, positional encoding, and contrastive learning, leveraging graph patches and embedding patches. By partitioning large-scale knowledge graph data into graph patches and decomposing embeddings, alongside the incorporation of contrastive learning techniques, the proposed model effectively extracts the local and global node information from different views. Experimental evaluations conducted on two real-life educational datasets (COCO and Xuetang) demonstrate the effectiveness of the proposed model in improving recommendation accuracy and enhancing performance.