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Today a tremendous amount of geospatial knowledge is hidden in massive volumes of text data. To facilitate flexible and powerful geospatial analysis and applications, we introduce a new architecture: geospatial knowledge hypercube, a multi-scale, multidimensional knowledge structure that integrates information from geospatial dimensions, thematic themes and diverse application semantics, extracted and computed from spatial-related text data. To construct such a knowledge hypercube, weakly supervised language models are leveraged for automatic, dynamic and incremental extraction of heterogeneous geospatial data, thematic themes, latent connections and relationships, and application semantics, through combining a variety of information from unstructured text, structured tables, and maps. The hypercube lays a foundation for many knowledge discovery and in-depth spatial analysis, and other advanced applications. We have deployed a prototype web application of proposed geospatial knowledge hypercube for public access at: https://hcwebapp.cigi.illinois.edu/.