Recent advancements in neuroimaging have made new ways of breaking down the dynamic interplay of genes and environmental influences, which affect the structure of accessible brain development. The impact on language abilities and verbal short-term memory of pre-school children from socioeconomic status (SES). Children from lower SES show less linguistic abilities and communication skills compared to higher SES children. The challenges of children's vocabulary brain development difference between lower and higher SES continues sometimes grows with age. Therefore this paper, machine learning assisted vocabulary development framework (MLVDF), has proposed a multimodal assessment of young children's brains' intellectual ability and capabilities. The proposed methodology is focused on the estimation and comparison with more developed strategies of nonverbal abilities using similar video interfaces. Machine learning technologies are used to determine a minimum number of variables capable of predicting particular children's cognitive skills. Thus, the findings highlight the complex effect of verbal and cognitive abilities on children's language and life experience. The results are obtained by the analysis of SES in Children's brain development as Cognitive skills ratio is 86.6%, Verbal abilities ratio is 87.12%, Children's brain capability ratio is 87.6%, Increasing the memory ratio is 83.5%, Learning attitude ratio is 93.8%, Reduce the effect of socioeconomic status ratio is 84.25%, Reading efficiency ratio is 87.6%.

神经影像学的最新进展为打破基因和环境影响的动态相互作用提供了新的方法，这些相互作用影响了大脑发育的结构。社会经济地位（SES）对学龄前儿童语言能力和言语短期记忆的影响。与较高 SES 的儿童相比，来自较低 SES 的儿童表现出较低的语言能力和沟通技巧。儿童词汇大脑发展的挑战在较低和较高 SES 之间的差异有时会随着年龄的增长而增长。因此，本文，机器学习辅助词汇发展框架（MLVDF），提出了一种对幼儿大脑智力和能力的多模态评估。所提出的方法侧重于使用类似的视频界面对非语言能力的更发达策略进行估计和比较。机器学习技术用于确定能够预测特定儿童认知技能的最少变量数量。因此，研究结果强调了语言和认知能力对儿童语言和生活经验的复杂影响。通过SES对儿童大脑发育的分析得出的结果为：认知技能比率为86.6%，语言能力比率为87.12%，儿童大脑能力比率为87.6%，记忆能力比率为83.5%，学习态度比率为93.8% ，降低社会经济地位的效果比为84.25%，阅读效率比为87.6%。机器学习技术用于确定能够预测特定儿童认知技能的最少变量数量。因此，研究结果强调了语言和认知能力对儿童语言和生活经验的复杂影响。通过SES对儿童大脑发育的分析得出的结果为：认知技能比率为86.6%，语言能力比率为87.12%，儿童大脑能力比率为87.6%，记忆能力比率为83.5%，学习态度比率为93.8% ，降低社会经济地位的效果比为84.25%，阅读效率比为87.6%。机器学习技术用于确定能够预测特定儿童认知技能的最少变量数量。因此，研究结果强调了语言和认知能力对儿童语言和生活经验的复杂影响。通过SES对儿童大脑发育的分析得出的结果为：认知技能比率为86.6%，语言能力比率为87.12%，儿童大脑能力比率为87.6%，记忆能力比率为83.5%，学习态度比率为93.8% ，降低社会经济地位的效果比为84.25%，阅读效率比为87.6%。研究结果强调了语言和认知能力对儿童语言和生活经验的复杂影响。通过SES对儿童大脑发育的分析得出的结果为：认知技能比率为86.6%，语言能力比率为87.12%，儿童大脑能力比率为87.6%，记忆能力比率为83.5%，学习态度比率为93.8% ，降低社会经济地位的效果比为84.25%，阅读效率比为87.6%。研究结果强调了语言和认知能力对儿童语言和生活经验的复杂影响。通过SES对儿童大脑发育的分析得出的结果为：认知技能比率为86.6%，语言能力比率为87.12%，儿童大脑能力比率为87.6%，记忆能力比率为83.5%，学习态度比率为93.8% ，降低社会经济地位的效果比为84.25%，阅读效率比为87.6%。