Clustering is an unsupervised learning method that measures the similarity and difference between data by analyzing sample features. It utilizes the characteristics of high intra cluster similarity and large inter cluster differences to automate the process of grouping data. It is widely used in fields such as computer vision, text mining, biological information and so on. There is still improvement room in clustering algorithms in terms of robustness, universality, and class number selection, and the effectiveness of the algorithms is largely influenced by the density and manifold of the dataset. This paper proposes a robust evolutionary clustering algorithm based on local structure self-expression. This algorithm uses radial basis functions and adds prior information to obtain local density difference features of the data, constructing a new similarity measure. In this process, the extraction mechanism of local structural features of data and the recognition mechanism of stable classes are integrated, making clustering more robust and universal. Dynamic evolutionary clustering has natural advantages in these two aspects, which can continuously optimize clustering results during the dynamic clustering process, resulting in significant improvements in clustering performance. The new algorithm integrates local and global features through self-expression of the structure information in the dataset, while monitoring the stability of the class during dynamic evolution, in order to obtain better final clustering results. The experimental results on both synthetic and real datasets demonstrate that the clustering performance of the new algorithm is superior.