To achieve better robustness of the deep clustering algorithm when facing complex data of different types, a selective ensemble strategy with the deep clustering algorithm was combined, proposing a deep clustering algorithm with fusion selective clustering ensemble. This approach effectively enhanced the robustness and clustering performance of the deep clustering algorithm. The algorithm utilized an autoencoder-based deep clustering algorithm with different initialization parameters to generate multiple diverse base clustering results. It constructed measures for ensemble similarity and diversity of base clusterings. A certain number of base clusterings with higher similarity and richer diversity were selected as candidates for clustering ensemble. The clustering ensemble strategy considers the reliability of clusters to construct a weighted graph consensus function. Experimental results demonstrate that the deep clustering algorithm with fusion selective clustering ensemble shows improved robustness and achieves better clustering results on various types of data compared to many existing clustering ensemble algorithms.