Objective: To investigate the relationship between symptoms, exercise capacity and hyperinflation in chronic obstructive pulmonary disease (COPD) patients; and to try to establish a nomogram prediction model based on spirometry to predict the occurrence of hyperinflation in COPD. Methods: COPD patients and controls with normal lung function were recruited to perform the 6-minute walk test (6MWT). The COPD patients were divided into a hyperinflation group (39 patients) and a non-hyperinflation group (62 patients) according to the GOLD guidelines. Clinical data, 6MWT and lung function were analysed in the three groups. Variables were screened by the Boruta algorithm combined with Lasso regression, and the variables were analysed by univariate and multivariate logistic regression, to establish a nomogram prediction model of the occurrence of hyperinflation in COPD patients and to assess its predictive effect. Results: Compared to the non-hyperinflation group, patients in the hyperinflation group had a shorter 6-minute walk distance (6MWD), a smaller 6MWD as a percentage of predicted value (6MWD%pred), and a mean 6MWD%pred of （86.74 ± 12.54）%, accompanied by the symptoms of a more severe drop in walking pulse-oximetry and a more severe post-exercise leg fatigue. The hyperinflation group had a higher proportion of patients with mMRC ≥2 and CAT ≥10. The results of logistic regression analysis suggested that vital capacity(VC) (β=-2.636, OR=0.072 , 95% CI: 0.022 ~ 0.232), Maximal mid-expiratory flow of predicted(MMEF%pred) (β=-0.147, OR=0.863, 95% CI: 0.790 ~ 0.944) were independent predictors of the occurrence of hyperinflation in COPD patients. The mean values of VC and MMEF%pred in the hyperinflation group were （2.51 ± 0.60）L and （23.05 ± 6.48）%, respectively. The area under curve (AUC) of the reciever operating characteristic (ROC) curve of the nomogram model was 0.897 (95% CI: 0.836~ 0.957, P<0.01). The mean absolute error of the calibration curve was 0.023. Conclusion: The nomogram model based on the spirometer parameters VC and MMEF%pred can predict the occurrence of hyperinflation in COPD patients. When ventilatory dysfunction was present on spirometry testing in COPD patients, a decrease in the parameters VC and MMEF%pred predicted an increased risk of combined hyperinflation. These patients tend to have reduced exercise tolerance and are multisymptomatic, suggesting the need for clinical focus. This predictive model provides primary community care with an easy-to-implement, method of assessing patients with COPD and facilitates guidance for individualised treatment and rehabilitation.