The super-sized pore-throat network model can reflect both microscopic pore characteristics and macroscopic heterogeneity and is excellent in describing cross-scale flow fields. At present, there is no algorithm that can generate a micro pore-throat network model at a macro reservoir scale. This study examines algorithms for super-sized pore-throat network reconstruction using actual core sample data. It conducts a random simulation of mineral growth and dissolution under the constraints of four microscopic pore structure parameters: porosity, coordination number, pore radius, and throat radius. This approach achieves high-precision, super-sized, and regional pore-throat network modeling. Comparative analysis shows that these four parameters effectively guide the random growth process of super-sized pore-throat networks. The overall similarity between the generated pore-throat network model and real core samples is 88.7% on average. In addition, the algorithm can partition and control the generation of pore-throat networks according to sedimentary facies. The 100-megapixel model with 85,000 pores was generated in 455.9 s. This method can generate cross-scale models and provides a basis for cross-scale modeling in physical simulation experiments and numerical simulations.