BACKGROUND. Currently recommended traditional spirometry outputs do not reflect the relative contributions of emphysema and airway disease to airflow obstruction. We hypothesized that machine-learning algorithms can be trained on spirometry data to identify these structural phenotypes. METHODS. Participants enrolled in a large multicenter study (COPDGene) were included. The data points from expiratory flow-volume curves were trained using a deep-learning model to predict structural phenotypes of chronic obstructive pulmonary disease (COPD) on CT, and results were compared with traditional spirometry metrics and an optimized random forest classifier. Area under the receiver operating characteristic curve (AUC) and weighted F-score were used to measure the discriminative accuracy of a fully convolutional neural network, random forest, and traditional spirometry metrics to phenotype CT as normal, emphysema-predominant (>5% emphysema), airway-predominant (Pi10 > median), and mixed phenotypes. Similar comparisons were made for the detection of functional small airway disease phenotype (>20% on parametric response mapping). RESULTS. Among 8980 individuals, the neural network was more accurate in discriminating predominant emphysema/airway phenotypes (AUC 0.80, 95%CI 0.79–0.81) compared with traditional measures of spirometry, FEV1/FVC (AUC 0.71, 95%CI 0.69–0.71), FEV1% predicted (AUC 0.70, 95%CI 0.68–0.71), and random forest classifier (AUC 0.78, 95%CI 0.77–0.79). The neural network was also more accurate in discriminating predominant emphysema/small airway phenotypes (AUC 0.91, 95%CI 0.90–0.92) compared with FEV1/FVC (AUC 0.80, 95%CI 0.78–0.82), FEV1% predicted (AUC 0.83, 95%CI 0.80–0.84), and with comparable accuracy with random forest classifier (AUC 0.90, 95%CI 0.88–0.91). CONCLUSIONS. Structural phenotypes of COPD can be identified from spirometry using deep-learning and machine-learning approaches, demonstrating their potential to identify individuals for targeted therapies.