Objective:An artificial intelligence(AI)model based on deep learning algorithms was constructed using clinical data to evaluate the feasibility of predicting the efficacy of neoadjuvant immunotherapy combined with chemotherapy for non-small cell lung cancer(NSCLC).Methods:Clinical and pathological data of 132 patients with NSCLC who were diagnosed and treated with neoadjuvant immunotherapy combined with chemotherapy between January 2020 and January 2024 at Beijing Chest Hospital/Beijing Tuberculosis and Thoracic Tumor Research Institute were collected.Statistical analysis was conducted to identify the main factors affecting the efficacy of neoadjuvant im-munotherapy combined with chemotherapy.Variables were selected based on statistical results and relevant literature,and a variable data-set was constructed.A deep learning model was established using a multi-layer perceptron(MLP)algorithm with 5-fold cross-validation,and the performance of the model was evaluated using receiver operating characteristic curve(ROC).Results:Among the 132 patients,univari-ate analysis demonstrated statistically significant differences in sex(P=0.020),smoking history(P=0.004),carcinoembryonic antigen(CEA)(P=0.038)and programmed death-ligand 1(PD-L1)≥1%(P=0.038)between the major pathological response(MPR)and non-MPR groups.Patients in the complete pathological response(pCR)group and non-pCR groups showed statistical differences in tumor size(P=0.007)and CEA levels(P=0.010).After 5-fold cross-validation,the average area under the curve(AUC)of the MPR prediction model in the validation and test sets was 0.72 and 0.71,respectively.Conclusions:The deep learning model can effectively predict the efficacy of neoadjuvant chemoim-munotherapy in patients with NSCLC.