Spatial autocorrelation has been applied to extensive data obtained from many research projects, and it is a common method for studying spatial distribution. Spatial autocorrelation analysis is a means for analyzing data correlation based on the spatial correlation analysis. It studies the correlation between one polygon and the nearest neighboring polygon through recognizing the similar degree of the major spatial object and other spatial objects. Using the 2010 census data and applying the spatial statistics and GIS, the authors analyzed the models of population spatial distribution of 17 prefectures in Shandong province by such means as quarters of population density, Moran's I and Local Moran's I. The population spatial correlation shows that there are no abnormal spatial areas of high density and low density in prefectures of Shandong province. The population spatial correlation shown by Moran's I and Local Moran's I indicates that the population density spatial distribution has spatial cluster, high-high cluster and low-low cluster. The general population density spatial distribution has three spatial belt-shaped regions, where the highest density is in the southwest areas, the density decreases to the lowest density in the northeast areas, and the similar population density areas are centralized in the vicinage. There are 5 "high-high" prefectures centralized in the west and south areas, 4 "low-low" prefectures centralized in the north areas, 2 "low-high" relation isolated points existent in Laiwu and Rizhao and a "high-high" isolated point existent in Weifang.