The application fields of unbalanced data sets are becoming increasingly extensive, and the demand for them is getting higher. Taking the spectral clustering undersampling as a prerequisite, this study develops an unbalanced data mining method based on a self-encoding network to improve the classification accuracy of the overall data set. The clustering problem is converted into the multi-path partition problem of an undirected graph, and the spectral clustering is completed depending on the undirected graph and standardized processing. The majority of data sets are processed through selective undersampling to yield the classification boundary offset. The learning process is a self-encoding network of unsupervised learning, based on which the dimensionality of data is increased or reduced so that hidden features of each dimension can be obtained and the efficient representation and learning of data are realized at all levels. The self-encoding network is adjusted according to the comparison between the maximum mean difference and the preset threshold. The unbalanced data mining is then completed with the obtained classification interface. UCI data sets with different practical application backgrounds are selected, from which 10 sets of data are extracted as test sets. After spectral clustering undersampling, the simulation experiments demonstrate that the proposed method greatly improves the classification accuracy of the minority and overall mining performance, which shows good applicability and feasibility.