In-situ thermal upgrading significantly alters pore structure in low-maturity oil shales, yet the key mechanisms controlling this transformation remain to be defined. Deciphering pore geometry and seepage capacity is crucial, as they directly dictate the flow network connectivity and ultimate hydrocarbon recovery; however, systematic quantification of their dynamic anisotropic variations is currently lacking. We establish two novel two-dimensional coordinate mapping classification schemes based on geometric parameters extracted via machine learning. These represent a seepage capacity classification based on pore cross-sectional area (S) and perimeter (C), and a morphological classification based on circularity (Circ.) and aspect ratio (AR). Observations reveal a pronounced anisotropy in pore evolution. Bedding-normal (nominally vertical), both seepage capacity and morphological complexity follow non-monotonic trajectories driven by stress. Bedding-parallel (nominally horizontal), however, permeability decreases with the increasing number and proportion of low seepage capacity pores (L-SC), and the quantity of each morphological pore type increase continuously, although morphological complexity is diluted by newly formed simple pores. Fractal dimension (D) and Pearson correlation analysis further corroborate this anisotropy: throughout the heating process, D values in bedding-parallel direction (DH) remains consistently higher than in bedding-normal direction (DV). D values monotonically increase in the bedding-normal direction, showing a significant positive correlation with regular elongated pores; whereas bedding-parallel, D values fluctuate, exhibiting the strongest correlation with transitional pores. These newly established classification frameworks provide quantitative tools for characterizing the anisotropic evolution of pore architecture and offer theoretical support for efficient in-situ conversion of low-maturity oil shale.