Abstract: At present,the aging of China's population is becoming increasingly prominent.Accurate prediction of the number of the elderly population in the future is the basic work to consolidate the situation and policy research,which has important reference value for the formulation of relevant policies and social development.In this paper,a GM-LSTM model is proposed,which combines the gray system dynamic model with the advantages of LSTM deep learning neural network to build a composite model,and the LSTM neural network model is used to modify the residual of the estimated sequence and the original sequence in the GM prediction model.The model verification shows that the GM-LSTM model has good prediction accuracy and generalization ability.Using GM-LSTM model for analysis,data from 2008 to 2017 are selected for analysis to predict the number and density of the elderly population in all administrative areas of Nanjing from 2021 to 2035.The results show that in the next 15 years,the number of elderly population in each administrative area of Nanjing shows a trend of high base and high growth,and the elderly population density difference among administrative areas is significant.The elderly population density in the central urban area is relatively high,which makes it a densely populated area,and the population density gradually decreases along with the direction of the suburbs.

Key words: Aged tendency of population, Combined model, Grey prediction model, LSTM, Population prediction