Innovation Strategy of Cultivating Innovative Enterprise Talents for Young Entrepreneurs Under Higher Education - Yu, Xiao; Zhang, Baoge (Frontiers in Psychology, 2021)
A time series model is designed based on BP neural network (BPNN) to further optimize the innovation and development of new ventures. The specific situation of two factors are primarily analyzed: the supply and demand ratio of enterprise talents and the state of entrepreneurship in the development of new ventures. The results show that the potential demand of future enterprises for big data talents can be obtained by fitting prediction sequence. Based on Backpropagation Autoregressive Integrated Moving Average (BP-SARIMA) model, the post modeling and prediction are carried out, and the coefficient 0.6235 is obtained by substituting the equation of Pearson correlation coefficient. The analysis results suggest that the matching needs to be strengthened between the cultivation of innovative talents in universities and the demand trend of big data related positions in enterprises. Moreover, there is a mismatching between the cultivation of innovative talents and the demand of innovative talents. Meanwhile, the mental health level of young entrepreneurs is concerned. Through questionnaire survey and statistical data analysis, the mental health status of young entrepreneurs is compared with the national norm data. The results reveal that the mental health level of young entrepreneurs is significantly lower than that of the national norm, and the proportion of anxiety and depression is 29.4% and 27.5%, respectively. Considering the professional characteristics and competitive environment of young entrepreneurs, busy work and the multiple missions given by society to entrepreneurs are the major reasons for their pressure, and mental health problems are serious.