Using global spatial autocorrelation index and local spatial autocorrelation index, based on the vector data of contaminated sites in Zhejiang province, the overall spatial distribution and local clustering distribution of contaminated sites in the research area were analyzed in this paper. According to the vector data of contaminated sites and remote sensing data, the intelligent mapping feature integration method was constructed, such as designing map template, positioning map data frame and associated mapping auxiliary elements of contaminated sites in the research area. The results showed that: (1) the possibility of spatial clustering of contaminated sites in the research area is greater than 90%, with 34 statistically significant hotspots, 4 statistically significant high-high clustering areas, and 1 statistically significant high-low clustering area. (2) the intelligent mapping integration can complete the work of 3 days’ manual mapping within 8 minutes, which improved the mapping efficiency and accuracy significantly.