To address the issues in current OWL representation learning methods, which lack the ability to jointly represent complex semantic information across both the concept layer and the instance layer, an OWL representation learning approach using multi-semantic views of concepts, properties, and instances is proposed. The proposed method adopts a three-stage architecture including multi-semantic views partitioning, semantic-aware self-supervised post-training, and joint multi-task representation learning. First, MSV-KRL optimizes the mapping strategy from OWL to RDF graphs based on OWL2Vec*, and five fine-grained semantic view partitioning strategies are proposed. Subsequently, serialized post-training data is generated through the random walk and annotated attribute replacement strategy. The self-supervised post-training of the pre-trained model is then carried out to enhance adaptability to multi-semantic views. Finally, by employing a multi-task learning strategy, the complex semantic representation learning of concepts, properties, and instances in OWL graphs is achieved through joint optimization loss of multi-semantic view prediction tasks. Experimental results demonstrate that MSV-KRL outperforms baseline representation learning methods on multiple benchmarks. MSV-KRL can be adapted to multiple language models, significantly improving the knowledge representation capability of OWL’s complex semantics.