Construction of a Hierarchical Neural Network Power Source Model for Human Capital Technology Innovation and Benefit Distribution with Big Data Analysis

Abstract

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.