To quantify the O₃-induced damage non-destructively and rapidly, it is important to build up the relationship between the chlorophyll content and spectral characteristics. In this study, the winter wheat leaves under ozone stress were measured at the jointing, flowering and grain filling stages, respectively. The field experiment was conducted through the free-air ozone concentration elevation system (O₃-FACE) platform located at Jiangdu, Yangzhou, Jiangsu Province. Hyperspectral estimation of chlorophyll content under ozone stress was made using linear regression model, artificial neural network (ANN) model and partial least squares regression (PLSR) model, respectively. The results showed that the green peak of the leaf spectrum under ozone stress showed a "red shift", while the red edge of the leaves spectrum showed a "blue shift". Elevated ozone affected wheat leaves more at grain filling stage than jointing and flowering stages. There was a significant correlation between chlorophyll content and most spectral characteristics or vegetation spectral indexes under ozone stress with high estimation accuracy (R²>0.8). Among all models, the highest accuracy was achieved by the PLSR model based on spectral characteristics. The PLSR model can be used to estimate the chlorophyll content of winter wheat exposed to high ozone concentration and thus quantify the damage induced by ozone stress.