Deep neural network (DNN) has been widely used in various artificial intelligence applications and is, unsurprisingly, penetrating the field of school psychology. In the school environment, universal screening is used by teachers to identify children's emotional and behavioral risk (EBR) within a screener. EBR can be used to predict possible emotional and behavioral disorders, which impact children's educational and social outcomes. Using the BASC-2 Behavioral and Emotional Screening System Teacher Rating Scale (BASC-2 BESS TRS; Reynolds & Kamphaus (2004). "Behavior Assessment System for Children" (2nd ed.). Circle Pines, MN: American Guidance Service) norm data, we classified children's EBR status from normal to at-risk using DNN. Data oversampling was used to overcome the imbalanced sample feature (i.e., few cases with emotional and behavioral disorder). Traditional machine learning methods, such as Naïve Bayes and logistic regression, were included for comparison. The results indicated that the DNN with oversampling achieved the highest performance levels with accuracy (ACC) of 0.957, precision (PPV) of 0.545, true positive rate (TPR or sensitivity) of 1.000, and true negative rate (TNR or specificity) of 0.942 compared with the other methods. This novel method is helpful to provide accurate screening results for early identification of children's EBR. The current study provides a useful guide for researchers to apply the DNN and oversampling to classification in assessment-related research.