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Abstract: Subsurface rocks, as complex porous media, exhibit multiscale pore structures and intricate physical properties. Digital rock physics technology has become increasingly influential in the study of subsurface rock properties. Given the multiscale characteristics of rock pore structures, direct three-dimensional imaging at sub-micrometer and nanometer scales is typically infeasible. This study introduces a method for reconstructing porous media using multidimensional data, which combines one-dimensional pore structure parameters with two-dimensional images to reconstruct three-dimensional models. The pore network model (PNM) is stochastically reconstructed using one-dimensional parameters, and a generative adversarial network (GAN) is utilized to equip the PNM with pore morphologies derived from two-dimensional images. The digital rocks generated by this method possess excellent controllability. Using Berea sandstone and Grosmont carbonate samples, we performed digital rock reconstructions based on PNM extracted by the maximum ball algorithm and compared them with stochastically reconstructed PNM. Pore structure parameters, permeability, and formation factors were calculated. The results show that the generated samples exhibit good consistency with real samples in terms of pore morphology, pore structure, and physical properties. Furthermore, our method effectively supplements the micropores not captured in CT images, demonstrating its potential in multiscale carbonate samples. Thus, the proposed reconstruction method is promising for advancing porous media property research.