Risk assessment is critical to prevent psychological abuse of children as it can classify high-risk situations requiring action to protect children. Despite the widespread use of risk evaluation tools in child care, the prediction of mental health issues and the properties of the tools are consistent with increased predictive efficacy are still unclear. The psychological abuse of children is associated with an increased risk of depression. However, depression is present in many people who receive abuse. Hence in this paper, Artificial Neural Network-based Psychological Symptom Prediction Model (ANN-PSM) has been proposed to reduce depression and improve children's psychological level. The ANN-PSM patterns can detect the children increasingly likely to create psychological abuse in the face of sad signals and an increased risk of depression. Findings help to explain the mechanisms by which psychiatric depression puts children at risk for socioemotional adaptation. The experimental findings indicate that ANN's can be accepted more generally in clinical decision-making and achieve the highest detection rate of 97.84% depression among children.

风险评估对于防止对儿童的心理虐待至关重要，因为它可以对需要采取行动保护儿童的高风险情况进行分类。尽管在儿童保育中广泛使用风险评估工具，但心理健康问题的预测和工具的特性是否与增加的预测效力一致仍不清楚。对儿童的心理虐待会增加患抑郁症的风险。然而，许多受到虐待的人都患有抑郁症。因此，本文提出了基于人工神经网络的心理症状预测模型（ANN-PSM）来减少抑郁症，提高儿童的心理水平。ANN-PSM 模式可以检测到儿童在面对悲伤信号和抑郁风险增加时越来越有可能制造心理虐待。研究结果有助于解释精神抑郁症使儿童面临社会情绪适应风险的机制。实验结果表明，人工神经网络在临床决策中的接受度更高，儿童抑郁症检出率最高可达97.84%。