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To explore the influencing factors of respiratory function exercise behavior in patients with chronic obstructive pulmonary disease (COPD), and to construct a nomogram prediction model. 112 COPD patients in our hospital from March 2021 to March 2023 were selected, and patients were divided into low compliance group (n=68) and high compliance group (n=44) according to their respiratory function exercise behavior. The influencing factors of low compliance of respiratory function exercise behavior in COPD patients were analyzed by univariate analysis and multivariate logistic regression analysis, and a prediction model was constructed, the predictive value of the model was analyzed by receiver operating characteristic (ROC). Age≥ 60 years old, on the job, no family support, dyspnea after activity, high disease uncertainty, and low exercise self-efficacy were independent risk factors for low compliance of respiratory function exercise in COPD patients (P<0.05). The area under the curve (AUC) of age, on the job, family support, dyspnea after activity, disease uncertainty, exercise self-efficacy and nomogram prediction model were 0.645, 0.645, 0.717, 0.600, 0.660, 0.672 and 0.908 respectively, the prediction model had higher predictive value for low compliance of respiratory function exercise in COPD patients, when the cut-off value was 0.498, the sensitivity was 0.912 and the specificity was 0.795. The internal validation of the Bootstrap method (B=1000) showed that the predicted curve after the correction of the deviation was basically coincident with the ideal line, and the consistency index (C-index) was 0.816, indicating that the prediction ability of the model was better. The decision curve shows that the threshold probability range was 0.01~0.92, and the net return rate was>0. Age≥ 60 years old, on the job, no family support, dyspnea after activity, high uncertainty of disease, and low exercise self-efficacy are independent risk factors for low compliance of respiratory function exercise in COPD patients. The predictive model can help to assess exercise compliance and develop interventions. [ABSTRACT FROM AUTHOR]