Sedimentary microfacies identification is fundamental for reservoir characterization, directly influencing hydrocarbon exploration and production strategies. However, traditional methods relying on core analysis, seismic interpretation, and manual well‐log analysis face significant challenges: (1) high costs and limited coverage of coring data, (2) subjectivity in seismic facies interpretation, and (3) poor generalization of conventional machine learning models when trained on small datasets. To overcome these limitations, this study proposes Hopular—a novel deep learning architecture leveraging modern Hopfield networks. We validated the framework using 4000 normalized data points from 10 wells, covering eight logging parameters and five microfacies types. Evaluations across small (≤ 500 samples), medium (≤ 2000), and large (≥ 3000) datasets demonstrated robust performance, with R 2 scores of 0.704 (±0.021), 0.809 (±0.059), and 0.925, respectively. The model excels in capturing data relationships, particularly in small data regimes (11.6% R 2 improvement over ensemble methods). In summary, Hopular provides an accurate, data‐efficient solution for microfacies identification and supports exploration in data‐scarce settings. This work advances reservoir characterization by combining Hopfield networks’ associative memory with deep learning, offering reliable technical support for subsurface interpretation.