In this paper, a hierarchical neural network power source model is used to conduct an in-depth analysis and research on human capital technology innovation and revenue distribution. A hierarchical neural network analysis method was chosen to evaluate the human capital value of professional degree master students, and the applicability of the index system was confirmed through errors; moreover, the significance of the output results was analyzed according to the weight assignments of the input, implicit, and output layers. The analysis found that there was a large disagreement in the assessment of their human capital value, which led to the lack of practical utility of human capital. Knowledge-skilled talents have a wealth of theoretical knowledge and can use theories to guide related work. Compared with technically skilled high-skilled talents, their educational level is higher, and they can summarize past intuitive experience into theoretical guidance. Therefore, the hierarchical neural network method we constructed is theoretically effective in assessing the value of the human capital of professional master’s students and the role of the main constituents. Based on the assessment results, we can provide policy-informed suggestions for improving the quality of school education. To quickly verify whether the model can converge during the training process, a simple dataset with only two sequences and the elements in the sequences being real numbers rather than vectors are constructed to speed up the computation; meanwhile, the length of the sequences in this dataset is adjustable to initially verify the model’s ability to alleviate the long-time dependence problem.

中文翻译：

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基于大数据分析构建人力资本技术创新与利益分配的分层神经网络电源模型

本文采用层次神经网络电源模型对人力资本技术创新与收益分配进行深入分析研究。选择层次神经网络分析方法对专业学位硕士研究生的人力资本价值进行评价，通过误差确认指标体系的适用性；此外，根据输入层、隐式层和输出层的权重分配来分析输出结果的显着性。分析发现，他们对人力资本价值的评估存在较大分歧，导致人力资本缺乏实际效用。知识型人才具有丰富的理论知识，能用理论指导相关工作。与技术熟练的高技能人才相比，他们的教育水平较高，可以将过去的直觉经验总结为理论指导。因此，我们构建的层次神经网络方法在评估专业硕士生人力资本价值和主体作用方面在理论上是有效的。根据评估结果，我们可以为提高学校教育质量提供政策性建议。为了在训练过程中快速验证模型是否能够收敛，构建了一个只有两个序列的简单数据集，序列中的元素为实数而不是向量，以加快计算速度；同时，该数据集中序列的长度是可调的，以初步验证模型缓解长期依赖问题的能力。