Objective     To explore the clinical value of artificial intelligence (AI) quantitative parameters of pulmonary ground-glass nodules (GGN) in predicting the degree of infiltration. Methods     A retrospective analysis of 168 consecutive patients with 178 GGNs in our hospital from October 2019 to May 2021 was performed, including 43 males and 125 females, aged 21-78 (55.76±10.88) years. Different lesions of the same patient were analyzed as independent samples. Totally, 178 GGNs were divided into two groups, a non-invasive group (24 adenocarcinoma in situ and 77 minimally invasive adenocarcinoma), and an invasive group (77 invasive adenocarcinoma). We compared the difference of AI quantitative parameters between the two groups, and evaluated predictive valve by receiver operating characteristic curve and binary logistic regression model. Results     (1) Except for the gender (P=0.115), the other parameters, such as maximal diameter [15.10 (11.50, 21.60) mm vs. 8.90 (7.65, 11.15) mm], minimum diameter [10.80 (8.85, 15.20) mm vs. 7.40 (6.10, 8.95) mm], proportion of consolidation/tumor ratio [13.58% (1.61%, 63.76%) vs. 0.00% (0.00%, 0.67%)], mean CT value [–347.00 (–492.00, –101.50) Hu vs. –598.00 (–657.50, –510.00) Hu], CT maximum value [40.00 (–40.00, 94.50) Hu vs. –218.00 (–347.00, –66.50) Hu], CT minimum value [–584.00 (–690.50, –350.00) Hu vs. –753.00 (–786.00, –700.00) Hu], danger rating (proportion of high-risk nodules, 92.2% vs. 66.3%), malignant probability [91.66% (85.62%, 94.92%) vs. 81.81% (59.98%, 90.29%)] and age (59.93±8.53 years vs. 52.04±12.10 years) were statistically significant between the invasive group and the non-invasive group (all P<0.001). (2) The highest predictive value of a single quantitative parameter was the maximal diameter (area under the curve=0.843), the lowest one was the risk classification (area under the curve=0.627), the combination of two among the three parameters (maximal diameter, mean CT value, and consolidation/tumor ratio) improved the predictive value entirely. (3) Logistic regression analysis showed that maximal diameter and mean CT value both were the independent risk factor for predicting invasive adenocarcinoma. (4) When the threshold of v was 1.775%, the diagnostic sensitivity of invasive adenocarcinoma was 0.753 and the specificity was 0.851. Conclusion     AI quantitative parameters can effectively predict the degree of infiltration of GGNs and provide a reliable reference basis for clinicians.