Clustering has aroused much attention in the community of data mining and image processing. However, it has the following two problems, which greatly limit its applications: 1) Existing methods usually adopt unbalanced clustering structures, and the feature information of categories with too few samples cannot be fully expressed and utilized, thus affecting the accuracy of clustering. 2) The continuous pseudo-label matrix learned from the relaxed problem based on spectral analysis deviates from reality to some extent. To solve the above problems and improve the clustering performance, this paper proposes a novel method named balanced clustering with discretely weighted pseudo-label (BC_DWP). Initially, the balanced constraint is employed for canonical clustering, which can generate balanced clusters through minimization. Then, the weighted pseudo-label matrix with discrete features is introduced to avoid the trivial solution of unsupervised least squares regression. After that, the \(l_{2,p}\) -norm is introduced to satisfy the row sparsity of the selection matrix with flexible p. Finally, an efficient iterative algorithm is provided to optimize the model. Experimental results on six datasets show that the proposed method can not only handle the large-scale data, but also produce good clustering performance.