Abstract: Objective: To explore the value of the artificial intelligence (AI) volumetric density method in determining the invasion of pulmonary hyposolid nodules (SSNs). Methods: A total of 108 SSNs and the pathological results of 106 patients were reviewed, and these were divided into a glandular prodromal lesions group and an adenocarcinoma group. Pulmonary nodule AI software was used to measure and compare the CT quantitative parameters of the two groups, including the maximum CT value, minimum CT value, average CT value, kurtosis, skewness, Perc.25%, Perc.50%, Perc.75%, Perc.90%, nodule volume, and mean nodule diameter. Moreover, a receiver operating characteristic curve (ROC) was obtained by MedCalc software to evaluate the sensitivity, specificity, positive predictive value, and negative predictive value for the diagnosis of SSN infiltration, and their diagnostic performance was evaluated by logistic regression analysis. Results: There were significant differences in most CT quantitative parameters of SSNs. The highest diagnostic efficiency was Perc.25% and the AUC was 0.797, while the AUC was 0.787 for Perc.50% and the mean CT value. Logistic regression analysis showed that Perc.25% with the highest diagnostic efficiency was combined with Perc.50% and the mean CT value. The model with Perc.25% and the mean CT value had the highest diagnostic efficiency, and the combined diagnostic model had a higher diagnostic efficiency than Perc.25% and the mean CT value alone. According to MedCalc software, SSNs with Perc.25% ≥−578 HU and mean CT values ≥ −468 HU were more likely to be in the adenocarcinoma group. In this study, Perc.25% was combined with the mean diameter of nodules, and a very valuable combined diagnostic model II was obtained to judge the infiltration of SSNs. Conclusion: The AI volume density method has a high diagnostic value for SSN invasion. Moreover , the combination of Perc.25% and mean CT value can accurately judge the invasion than the use of average CT value alone, providing a quantitative basis for the clinical management of SSNs.