Objective To investigate the relationship between coronary artery plaque AI quantitative parameter and FFR-CT in coronary computed tomography angiography. Methods A total of 84 patients suspected of having CAD ［52 males and 32 females， aged 27 to 81 years with a mean age of （58.1 ± 11.9） years］ were enrolled in this study. All patients underwent coronary computed tomography angiography. The CCTA data was processed using shukun（SK） software for labeling and analysis of the coronary arteries， as well as obtaining quantitative parameters of coronary artery plaque AI and corresponding FFR-CT values. The quantitative parameters included plaque length， total volume， minimum lumen area （MLA）， minimal lumen degree （MLD）， lipid composition volume and proportion， fibrous-lipid composition volume and proportion， fibrous composition volume and proportion， calcified composition volume and proportion. Coronary artery hemodynamic abnormality or myocardial ischemia was defined as an FFR-CT value ≤ 0.8. Correlational analysis was performed to evaluate the association between AI plaque quantitative parameters and FFR-CT values. Univariate and multivariate binary logistic regression analyses were conducted to identify independent risk factors for predicting FFR-CT ≤ 0.8. The predictive performance of the model based on AI plaque quantitative parameters was assessed using receiver operating characteristic （ROC） curve analysis and calculation of the area under the curve （AUC）. Sensitivities， specificities， diagnostic test accuracy rates were also calculated. Results The predominant symptoms observed in the cohort of 84 patients were chest pain （n = 39， 46.4%） and distress （n = 27， 32.1%）. Spearman analysis results revealed a weak positive correlation between FFR-CT and MLA （r = 0.49， P < 0.000 1）， while weak negative correlations were found for plaque length， total volume， lipid composition volume， fibrous-lipid composition volume， fibrous composition volume， and calcified composition volume （r = -0.44， -0.56， -0.40， -0.36， -0.42， -0.40； all P < 0.05）. Additionally， MLD exhibited a moderate negative correlation with FFR-CT （r = -0.60， P < 0.000 1）. In the univariate binary logistic regression analysis， several variables including plaque length， total volume， MLA， MLD， lipid composition volume， fibrous-lipid composition volume， fibrous composition volume， and calcified composition volume were found to be independently associated with FFR-CT ≤ 0.8 （All P < 0.05）. The adjusted multivariate binary logistic regression analysis model revealed that MLD was the sole independent predictor （OR= 1.082， 95%CI：1.034 ~ 1.133， P = 0.001）. The logistics regression model expression was logit（P）=0.079X₁ - 4.052， where X₁ represents the value of MLD and achieved a predictive accuracy of 85.2%. The ROC AUC of plaque length， total volume， MLA， MLD， lipid composition volume， fibrous-lipid composition volume， fibrous composition volume and calcified composition volume were 0.796， 0.886， 0.711， 0.754 and 0.698 respectively， and the coresponding sensitivities and specificities were 47.83%， 73.91%， 73.90%， 52.17%， 60.87% and 92.11%， 73.68%， 60.53%， 84.21%， 89.47%. The five indexes combined diagnostic model possessed the largest AUC of 0.906， and 73.91%， 71.05% of sensitivity and specificity. Conclusion The AI quantitative parameters of coronary artery plaque exhibited varying degrees of correlation with FFR-CT， while MLD emerged as the sole independent predictor of FFR-CT ≤ 0.8， demonstrating high diagnostic efficiency.