In the era of big data, high-resolution Earth observation technologies have been able to provide the most authentic, quantitative, comprehensive-coverage, and fast-updating data about the geographic phenomena and processes on the Earth's surface. Such data provide precise spatiotemporal benchmarks of information aggregation and computation of data mining for new developments of geospatial cognitive research. Geo-parcels are abstract expressions for mapping geographical entities from image-space to geographic-space. Geo-parcels are the smallest units of pattern mining with the construction of geographic scenes and loading various geospatial information. In this paper, a synergistic calculation mechanism with the machine learning methods of visual simulation and symbol inference were analyzed based on the basic unit of geo-parcels. From the dimensions of space, time, and attribute, we constructed an intelligent computation model based on geo-parcels by integrating three sub-models: zoning-stratified perception, spatiotemporal synergistically inversion, and multi-granular decision-making. Furthermore, this paper explored the pattern mining methods of geo-parcels for their distribution, growth, and function via two case studies: the agricultural planting structure mapping in Xifeng County, Guizhou province and the planning decision in Jiangzhou District of Guangxi Zhuang Autonomous Region.