Objective To explore the value of geriatric nutritional risk index as a prognostic marker for early lung cancer patients and the construction of a decision tree prediction model. Method The retrospective analysis of 138 patients with early lung cancer were collected in West China Hospital from January 2021 to December 2022 and divided into two groups according to the prognosis of patients namely poor prognosis n = 21 and good prognosis n = 117 . Comparing the general data binary Logistic regression analysis and decision tree analysis affected the prognosis independent factors and the model was verified internally using Bootstrap method. Result The GNRI of patients with good prognosis was higher than that of patients with poor prognosis P<0. 05 . According to binary logistic regression analysis age ≥ 75 years old pleural involvement preoperative anemia history and GNRI value ≤ 97. 35 were independent factors affecting the prognosis of early lung cancer surgery P< 0. 05 . At the same time using four statistically significant variables from single factor analysis as predictive factors a decision tree model was constructed using SPSS software. Among them age was the main factor and the detected predictive value was 91. 1% Mainly involving pleura with a predicted detection value of 88. 6% Based on a history of preoperative anemia the predicted detection value is 90. 5% Based on GNRI values the predicted detection value is 73. 2%. The model was internally validated using Bootstrap. Result To simulate discrimination with an AUC of 0. 872 and a 95%CI of 0. 805-0. 938. According to the ROC curve model analysis the GNRI area plot is 0. 903 with a 95%CI of 0. 842-0. 963 a sensitivity of 90. 5% and a specificity of 79. 5%. Conclusion The nutritional risk index can predict the prognosis of early lung cancer patients undergoing surgery and the prognosis is also influenced by the patient ' s age pleural involvement and preoperative anemia history. By establishing a decision tree model that is easy to operate and visually effective the prognosis can be predicted as early as possible providing scientific basis for treatment plan formulation and prognosis evaluation.