Objective：To conduct an in-depth analysis of the key factors influe-ncing the risk of malnutrition in elderly diabetic patients with cognitive frailty（CF）and to construct an accurate risk prediction nomogram model based on these factors. Methods：A cross-sectional study design was adopted，enrolling 124 elderly diabetic patients with CF. The patients’frailty，cognitive function， psychological state and risk of malnutrition were evaluated. Multivariate logistic regression analysis was used to identify influencing factors for malnutrition risk，and the R language was used to construct the prediction model and draw the nomogram. Model validation was carried out by receiver operating characteristic curve analysis，Hosmer-Lemeshow test and concordance index evaluation，and calibration curve drawing. Results：The malnut-rition risk rate among the 124 elderly diabetic patients with CF was 67.7%（84/124）. These 84 patients were assigned to the malnutrition risk group，and the remaining 40 were assigned to the well-nourished group. Age， marital status，body mass index（BMI），geriatric depression scale（GDS）score，albumin，and prealbumin levels were identified as independent predictors of malnutrition risk in elderly diabetic patients with CF（all P ＜ 0.05）. The nomogram model constructed based on these influencing factors had a C-index of 0.781（95% CI：0.695-0.867）. The Hosmer-Lemeshow test indicated a good fit for the nomogram model. Decision curve analysis showed that when the threshold probability ranged from 0.10 to 0.67，the net benefit rate of using this nomogram model to predict malnutrition risk in elderly diabetic patients with CF was 0.46-0.60. Conclusion：In elderly diabetic patients with CF，age，marital status，BMI，GDS score，albumin，and prealbumin levels are key factors influencing the risk of malnutrition. The established risk prediction model demonstrates moderate predictive performance and good clinical application value for assessing the risk of malnutrition in such patients.