The location of high⁃speed railway stations has always been in a contradiction. The government should not only reduce the impact of high⁃speed railway stations on people's lives, but also consider the cost of demolition. How to make the location of high⁃speed railway stations more scientific has become a problem that needs careful consideration. Therefore, the model of economic contribution degree was established by using linear regression analysis and grey prediction method to obtain the economic contribution degree of high⁃speed railway station to the city;based on the relevant data of high⁃speed railway in Liaoning Province, considering the construction cost, construction time, transfer convenience and economic contribution, a high⁃speed railway station location evaluation model based on principal component analysis was proposed and the evaluation model was used to analyze the siting of three high⁃speed railway stations in Liaoning Province. The results show that the siting of Shenyang North Railway Station is the most successful,and the passenger flow and distance from the city center are the important factors affecting the development of high⁃speed railway.

This experiment focuses on the fragile zone in Mu Us Desert where farming, forestry, animal husbandry interlacing ecologically in desertification research as the object. It studies the optimal computer automatic classification of Mu Us Desert based on CBERS and TM remote sensing image data type, and investigates the evolution process of Mu Us Desert (20002013) combined with the evolution of GIS spatial analysis and landscape index quantitative. The experiment shows that, through the maximum likelihood supervised classification method, the highest overall precision of sand type obtained is above 86.21%, which is the ideal means of desertification land classification. A nonlinear relationship exists between environment changes and shifting sands, semishifting sands, semifixed sands，fixed sands. Those four types of sand plaques present to be instability, in which shifting and semishifting sands change significantly in position, going on gradually from southwest to northwest and south. Succession between patches will still occur frequently in a period of time in the future, while the percentage of the four types of sands accounts from 82.29% to 75.07% of the study area, proving the ecological environment is getting better by the governance of Mu Us Desert.