关键词: 科技产出 , 影响因素分析 , 多元线性回归分析 , BP神经网络 , 二元相关分析 , 预测 , PCT专利申请 , SCI论文产出 , Scientific and technological outputs, Impact factors analysis, Multiple linear regression, BP neural network, Bivariate correlation analysis, Prediction, PCT patent applications, SCI papers productivity

Abstract: Firstly some qualitative analysis methods such as literature research and network investigation are applied to find out all the possible factors influencing scientific and technological(S&T) outputs, and considering data availability, collect all related data to S&T productivity and their influencing factors for the period 1996-2008. Then based on the collected data, a bivariate correlation analysis method is utilized to analyse the mutual relations between S&T outputs and their influencing factors, and with the multiple linear regression method selecting the high-influencing factors to construct a model analyzing influencing factors and prediction for S&T outputs. Lastly based on the results of bivariate correlation analysis, a currently prevalent BP neural network prediction method is used to do a prediction study on S&T outputs, and compare the predictive performance with that of multiple linear regression method.