Recent years have witnessed a proliferation of studies aimed at developing clinical models capable of predicting lymph node metastasis (LNM) in early gastric cancer (EGC), yet tools for prediction grounded in the Lauren classification remain scarce.Data of 6468 patients diagnosed with EGC from fifteen Chinese high-volume cancer centers between January 2005 and December 2015 were retrospectively analyzed. Utilizing multivariate logistic regression analysis and the multilayer perceptron (MLP) prediction algorithm, a nomogram and an artificial neural network (ANN) model were developed and validated, respectively, for predicting the likelihood of LNM in non-intestinal EGC cases. The models' performances were evaluated and a comparative analysis of their parameters was undertaken. Subsequently, in-depth risk stratification analyses were performed around the two models.Non-intestinal type EGC demonstrated an elevated LNM rate and inferior prognosis compared to the intestinal type. Both nomogram and ANN model were developed and performed well in discrimination, calibration and clinical utility. Notably, the ANN model surpassed the nomogram in specificity (95.8% vs. 71.3%, P < 0.001), positive predictive value (PPV) (62.0% vs. 36.2%, P < 0.001) and overall accuracy (82.7% vs. 70.5%, P < 0.001). Patients with different risk strata derived from the nomogram, ANN model, and their combined application exhibited significantly different outcomes. The extent of lymph node dissection significantly influenced prognoses in high-risk patients identified by the combined model, whereas the anatomical location of metastatic lymph nodes did not.The ANN model established in this study can screen the patients at high risk of LNM in non-intestinal type EGC more accurately. Considering the high extragastric LNM rate observed in the high-risk stratum, radical gastrectomy combined with D2 lymph node dissection is strongly recommended.Copyright © 2025 The Author(s). Published by Wolters Kluwer Health, Inc.