Abstract: Multi-view subspace clustering aims to explore rich information across views to guide the clustering process. The key lies in effectively learning the unified representation and subspace representation between views. Recently, deep clustering methods have achieved promising effects due to the powerful representation capability of neural networks. However, the multi-source heterogeneity inherent in multi-view data allows existing methods to encode each view independently with a unimodal encoder, increasing the number of model parameters and limiting the model's generalization capability. Besides, low-rank subspace representation has been shown to facilitate clustering performance, while traditional nuclear norm regularization does not consider the difference between different singular values, leading to biased estimation. To tackle these two problems, we propose a novel multi-view unified representation learning network (namely, MURLN) for subspace clustering. Specifically, MURLN first uses the Transformer as the encoder architecture, which projects different views into the low-dimensional feature space with the same mapping rule by sharing parameters. In addition, a weighted fusion strategy for intra-view samples is conducted to learn a unified representation rationally. Finally, the weighted Schatten p-norm is introduced as the low-rank constraint of the subspace representation matrix. Extensive experiments on seven multi-view datasets verify the effectiveness and superiority of our proposed method.