Traditional cycloplegic refractive power detection with specific lotions dropping may cause side-effects, e.g., the pupillary retraction disorder, on juvenile eyes. In this paper, we develop a novel neural network algorithm to predict the refractive power, which is assessed by the Spherical Equivalent (SE), using real-world clinical non-cycloplegic refraction records. Participants underwent a comprehensive ophthalmic examination to obtain several related parameters, including sphere degree, cylinder degree, axial length, flat keratometry, and steep keratometry. Based on these quantitative biomedical parameters, a novel neural network model is trained to predict the SE. On the whole age test dataset, the domain knowledge embedding network (DKE-Net) prediction accuracies of SE achieve 59.82% (between ± 0.5 D ), 86.85% (between ± 1 D ), 95.54% (between ± 1.5 D ), and 98.57% (between ± 2 D ), which demonstrate superior performance over conventional machine learning algorithms on real-world clinical electronic refraction records. Also, the SE prediction accuracies on the excluded examples that are disqualified for model training, are 2.16% (between ± 0.5 D ), 3.76% (between ± 1 D ), 6.15% (between ± 1.5 D ), and 8.78% (between ± 2 D ). This is the leading application to predict refraction power using a neural network and domain knowledge, to the best of our knowledge, with a satisfactory accuracy level. Moreover, the model can also assist in diagnosing some specific kinds of ocular disorders.