Measuring financial performance has become an essential topic due to the potential decimating impacts on the corporation itself as well as to whole societies during financial turmoil. In order to provide an overarching description of the multidimensional nature for measuring a corporation’s operations, it is preferable to employ data envelopment analysis (DEA). Different from prior research that merely focuses on a singular DEA performance rank, this study extends it to multiple DEA specifications (i.e., it combines inputs and outputs in several different ways) so as to make judgments more complete and robust. We also execute fuzzy visualization technique (i.e., nonlinear fuzzy robust principal component analysis, NFRPCA) to represent the main characteristics of data so that non-specialists can have better access to the results. The analyzed result is then fed into the restricted Boltzmann machine (RBM) to establish a model to forecast a firm’s operating performance. Even a fraction of accuracy improvement can result in considerable future savings to a firm and investors. When examined using real cases, the model is a promising alternative for operating performance forecasting and can assist both internal and external market participants.