<p>In the realm of machine learning algorithms based on the framework of ordered pair of normalized real numbers (OPNs), there is a sharp increase in the number of model feature variables when real number data are converted to the corresponding OPNs form. For real number data, the conversion results in a vast search space for the model. If these OPNs variables are entered into the machine learning model without selection, the model is prone to errors due to the large search space, and its performance will suffer dramatically. This study introduces a stepwise regression algorithm within the OPNs framework, applying binary fundamental operation units and their methods of operation from OPNs theory to stepwise regression. We select different variable screening criteria as needed, remove redundant OPNs variables, and extract significant OPNs variables to include in the regression equation. At the same time, we exclude those variables that have insignificant effects, thereby reducing unnecessary redundant information and constructing an 'optimal’ OPNs regression analysis model. We are dedicated to identifying the optimal or near-optimal OPNs variable inputs, minimizing the input of the model, and improving the performance of the algorithm. We then investigate the patterns of real number data characteristics paired as OPNs variable features, which reduces the size of input data and computational load for machine learning models based on the OPNs theory. Compared to traditional algorithms, the OPNs stepwise regression (OPNs-SR) model demonstrates more information and better algorithmic results on most datasets.</p>