Deep multi-view clustering aims to exploit the rich semantic information contained in heterogeneous multi-view data to uncover the underlying relationships among samples. However, existing deep multi-view clustering models often overlook inter-cluster separability and the effective integration of semantic information across views, resulting in insufficient feature discriminability and consequently limited clustering performance. To address the above issues, this paper proposes a novel deep multi-view clustering method via cluster-semantic guidance. We separate clusters to enhance inter-cluster discriminability, while incorporating a knowledge distillation mechanism to ensure cluster stability and facilitate the learning of clustering-friendly representations. Furthermore, by aggregating sample-level semantic information, the model is guided to follow a cluster-oriented learning strategy that promotes the extraction of discriminative features, thereby strengthening the sample representation capability. Our method effectively learns discriminative and clustering-friendly representations, guiding the model to acquire distinctive feature embeddings from a cluster-oriented perspective. Our comprehensive experiments across datasets of varying scales confirm the model's effectiveness, showing superior clustering performance over existing state-of-the-art methods.