To analyze the relationship between peripheral blood inflammation-nutritional parameters and pathological characteristics of patients with endometrial cancer based on automatic classifiers and its short - term prognostic predictive value. Method A retrospective study included 156 patients with endometrial cancer who underwent surgical treatment in the First Affiliated Hospital of Xinjiang Medical University from April 2020 to April 2024 the experimental group and 110 age-matched healthy women the control group . The fibrinogen - albumin ratio FAR prognostic nutritional index PNI lymphocyte-monocyte ratio LMR and systemic immune inflammation index SII were compared between the two groups. Further analyze the differences of each index under different pathological characteristics FIGO stage grade etc. in the experimental group. Based on the Modeler automatic classifier screen the prognostic influencing factors of patients with endometrial cancer analyze the relationship between peripheral blood inflammation-nutrition parameters and prognosis and explore its predictive value. Result The research results showed that there were significant differences in inflammation and nutritional indicators between the experimental patients and the control group. The FAR 0. 08±0. 02 vs 0. 07±0. 01 and SII 610. 04±90. 86 vs 425. 64±102. 12 of the experimental group were significantly higher than those of the control group P<0. 05 . However PNI 49. 61±8. 05 vs 52. 69±1. 57 and LMR 3. 96±0. 78 vs 4. 35±0. 51 were significantly lower than those of the control group P<0. 05 . Further analysis revealed that patients with positive vascular invasion or nerve invasion had higher FAR and SII than those with negative results and lower PNI and LMR than those with negative results. The FAR and SII of patients in FIGO stage II were significantly lower than those of patients in stage Ⅲ and IV P<0. 05 . Among the prognosis prediction models established through the Modeler algorithm neural networks with an accuracy rate of 96. 15% Bayesian networks 94. 87% and C5 decision trees 94. 23% all demonstrated excellent prediction performance with FAR being the most important predictor. Conclusion The inflammation-nutrition parameters in the peripheral blood of patients with endometrial cancer are significantly correlated with pathological characteristics. The accuracy rate of the prediction model constructed based on Modeler is over 94%. The combination of inflammation - nutrition indicators and pathological characteristics can be used as an effective prognostic evaluation tool.