School of Public Finance and Administration, Harbin University of Commerce, Harbin 150001, Heilongjiang, China.
Comput Intell Neurosci. 2022 Jun 3;2022:7019078. doi: 10.1155/2022/7019078. eCollection 2022.
With the advent of big data, statistical accounting based on artificial intelligence can realistically reflect the dynamics of labor force and market segmentation. Therefore, based on the combination of machine learning algorithm and traditional statistical data under big data, a prediction model of unemployment in labor force based on the combination of time series model and neural network model is built. According to the theoretical parameters, the algorithm of the two-weight neural network is proposed, and the unemployment rate in labor force is predicted according to the weight combination of the two. The outcomes show that the fitting effect based on the combined model is superior to that of the single model and the traditional BP neural network model; at the same time, the prediction results with total unemployment and unemployment rate as evaluation indexes are excellent. The model can offer new ideas for assisting to solve the unemployment of the labor force in China.
随着大数据时代的到来，基于人工智能的统计核算可以真实地反映劳动力和市场细分的动态。因此，基于大数据下机器学习算法和传统统计数据的结合，构建了基于时间序列模型和神经网络模型组合的劳动力失业预测模型。根据理论参数，提出了双权值神经网络算法，并根据两者的权重组合对劳动力失业率进行预测。结果表明，基于组合模型的拟合效果优于单一模型和传统 BP 神经网络模型；同时，以总失业率和失业率为评价指标的预测结果非常优秀。该模型可以为辅助解决中国劳动力失业问题提供新的思路。
