Intelligent employment rate prediction model based on a neural computing framework and human–computer interaction platform

An intelligent employment rate prediction model based on a neural computing framework and human–computer interaction platform is demonstrated in this manuscript. Predictive analytics is the future of things, and its significance is manifested in two main aspects: understanding the future so that people can prepare for its arrival, and predicting the current decision so that people can understand the possible consequences, and by the consequences of the analysis to determine the current decision, and strive to make the current decision. However, there are lots of challenges for the prediction tasks. The novelty of this research is mainly concentrated on two major aspects: (1) the neural network model is optimized and enhanced. The proposed nerve tree network model is essentially based on a tree-structured code for a multi-layered feed-forward sparse neural network; with the tree-structured code, the nerve tree network model does not require interconversion between its genotype and phenotype in the coding and decoding operations, and also effectively reduces the computing time. (2) The human–computer interaction is integrated to construct a user-friendly system. In interactive technology, the interactive contact surface and the model, interactive methods and social acceptance have also given rise to many questions that must be solved and problems that require further research and technological innovation. Through numerical verification, the performance of the proposed framework is validated, and the simulation proves the overall performance of the proposed model. Compared with other models, the proposed algorithms can achieve higher prediction accuracy.