Deep subspace clustering is a method that performs well in processing high-dimensional data clustering tasks. However， when dealing with challenging data， current deep subspace clustering methods with fixed self-expressive matrix usually exhibit suboptimal clustering results due to the conventional practice of treating self-expressive learning and indicator learning as two separate and independent processes， and the quality of self-expressive matrix has a crucial impact on the accuracy of clustering results. To solve the above problems， an enhanced deep subspace clustering method with unified framework was proposed. Firstly， by integrating feature learning， self-expressive learning， and indicator learning together to optimize all parameters， the self-expressive matrix was dynamically learned based on the characteristics of the data， ensuring accurate capture of data features. Secondly， to improve the effects of self-representative learning， class prototype pseudo-label learning was proposed to provide self-supervised information for feature learning and indicator learning， thereby promoting self-expressive learning. Finally， to enhance the discriminative ability of embedded representations， orthogonality constraints were introduced to help achieve self-expressive attribute. The experimental results show that compared with AASSC （Adaptive Attribute and Structure Subspace Clustering network）， the proposed method improves clustering accuracy by 1.84， 0.49 and 0.34 percentage points on the MNIST， UMIST and COIL20 datasets. It can be seen that the proposed method improves the accuracy of self-representative matrix learning， thereby achieving better clustering effects.