The multivariable spatial correlation analysis is mainly based on time series data and requires long-term time series for statistical analysis. The analysis of spatial non-stationary characteristics can determine the relationship between multivariate variables by using single a single dataset. Based on the spatially varying coefficient regression model, this study analyzed the spatial variation characteristics of the vegetation coverage index in response to precipitation and temperature in the Yili area of Xinjiang in 2006 and 2011. The regression coefficient map estimated by using the local linear geographical weighted regression(GWR) method further revealed that the spatial heterogeneity of the interaction between the variables. The results were compared against linear least squares regression method. The main findings are the following: (1)The spatially varying coefficient regression model can be used to analyze the spatial correlation between variables. (2) The local linear GWR is superior to the linear least squares regression method. (3) Results showed a clearly spatial non-stationary characteristics of the vegetation coverage index under the effects of precipitation and temperature in the Yili area of Xinjiang. (4) In addition to precipitation and temperature, factors such as topography, geomorphology and human activities can cause deviations in the estimation, which requires further research. This paper provides new ideas and methods to analyze spatial correlation between variables that exhibit spatial non-stationary characteristics and to obtain spatial simulation distribution of the vegetation coverage index.