The characterization of interwell section architecture is critical for revealing reservoir lateral heterogeneity and connectivity. This process integrates well and seismic data with geological knowledge yet faces inherent multiple solutions. Current characterization methods remain hampered by high levels of manual intervention, insufficient automation, and difficulties in evaluating the uncertainty of interwell section architecture. To address these challenges, this study presents an intelligent method for the automated characterization of reservoir architecture along section directions based on a Bayesian expert system. The approach quantifies domain knowledge via prior normal distributions. By utilizing well and seismic data, Bayesian probabilistic reasoning infers the guiding influence of each individual piece of domain knowledge on predicting the interwell distribution of architectural elements. A weighted ensemble decision framework then integrates these inferences to determine the interwell distributions of architectural elements and associated uncertainties. Case studies demonstrate that the method effectively evaluates uncertainty, generates geologically consistent section characterizations, achieves 81% consistency in blind well sand body predictions, and excels in delineating the lateral boundaries and contact relationships of architectural elements.