The change of population age structure is related to all aspects of the development of social production in China. It is beneficial to the adjustment of industrial structure and the construction of social security system by predicting the development of aging population in China. In order to predict the change of aging degree in China, based on the data of population aging in China from 1990 to 2018, using SPSS, MATLAB, Excel and other software, the paper forecasts the population aging degree from 2019 to 2023 by constructing Holt linear trend and BP neural network model. The model established by the model is tested, and the error analysis of the prediction results shows that the reliability of the prediction is high. Finally, according to the prediction of aging changes, the paper puts forward corresponding suggestions for the adjustment of industrial structure and the construction of social security system.

Yang Yang, Wang Jing. Prediction of Aging Degree Based on Holt Linear Trend and BP Neural Network. Academic Journal of Business & Management (2020) Vol. 2, Issue 2: 129-139. https://doi.org/10.25236/AJBM.2020.020214.

[1] Zhou Maojun, et al. Combination prediction of population aging trend in Anhui Province [J]. Journal of Hefei Normal University, 2018,36 (06): 25-28           
[2] Yang mengran. Prediction of population aging trend in Shanghai based on GM (1,1) model [J]. Economic Research Guide, 2019 (18): 48-49 + 134           
[3] Chen Taichang, you Shuai. Research on the development trend and strategy of population aging in the new era [J]. Scientific research on aging, 2019,7 (04): 11-20           
[4] Hu Qian. Labor supply effect of China's aging population: an analysis based on Provincial Panel Data [J]. Journal of Guangdong University of Finance and economics, 2019,34 (04): 33-48           
[5] Gao Bao. Research on evaluation method of learning ability change trend based on index smoothing model [J]. China forestry education, 2018,36 (05): 1-4   
[6] Wang Jinghui et al. Prediction of energy consumption in Anhui Province Based on Holt linear trend and ARIMA model [J]. Journal of Qiqihar University (NATURAL SCIENCE EDITION), 2019,35 (02): 82-86           
[7] Si Shoukui, sun Zhaoliang. Mathematical modeling algorithm and application [M]. Beijing: National Defense Industry Press, 2015.425-426           
[8] Zhu Jiaming, Hu Lingyan. A comparative analysis on the prediction of RMB exchange rate based on ARIMA and BP neural network -- Taking the exchange rate of USD and RMB as an example [J]. Journal of Chongqing University of science and Technology (NATURAL SCIENCE), 2019,33 (05): 207-212           
[9] Hu Lingyan, Zhu Jiaming. Comparative analysis of stock price prediction based on stepwise regression and BP neural network [J]. Journal of Liaoning University of Technology (NATURAL SCIENCE EDITION), 2019,39 (03): 201-205 + 210