A Forecast Model for Language Under the Influence of Immigration

Under the influence of globalization, population distribution of different languages has become a matter of universal concern. In this paper, the mater is considered from three aspects. First of all, Gray Model is used to predict the number of second language speakers. Then, the influencing factors are taken into account to predict the number of net immigrants through the BP neural network. Finally, while considering the impact of immigration, Logistic growth model are used to predict the population of native speakers. In order to analyze the demographic changes in different regions, firstly, logistic growth model is used to predict the population in different regions. Secondly, influencing factors are combined with the BP neural network to predict the number of net immigrants in the next 50 years. Based on the forecast, French will surpass the Punjabi, which ranks number nine to become the tenth language with the greatest number of native speakers. As for the total number of speakers using a particular language, native speakers plus second language speakers are considered. The Gray Model is used to predict the number of second language speakers in different regions over the next 50 years. Based on the forecast, no language in the current top-ten lists will be replaced by another language. Net immigration is used to reflect the changing global language distribution over the next 50 years. GDP per capita, education funds, medical expenses and tourism revenue are identified as four factors. BP neural network are used to find out the relationship between influencing factors and net immigrants, and then the number of net immigration of different regions in the next 50 years are predicted. Numerical results demonstrated that model in this paper is efficient and promising.