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Chlorophyll content is the main biochemical parameter of winter wheat, and its changes directly represent the photosynthetic capacity of winter wheat. Therefore, monitoring the chlorophyll content of winter wheat is of great significance for analyzing the photosynthetic capacity and growth status of winter wheat. Based on the canopy hyperspectral data and measured chlorophyll content of winter wheat on the ground, the correlation analysis between the measured chlorophyll content and the wavelet energy coefficient obtained from the original spectrum, fractional differential spectrum and original spectrum through continuous wavelet transform was carried out, and then the fractional differential spectrum and wavelet energy coefficient with good correlation were selected and combined with stepwise regression analysis the estimation model of chlorophyll content of winter wheat was established by using the methods of support vector machine and artificial neural network. The results showed that: at the jointing stage, booting stage, flowering stage and full growth stage, the results of continuous wavelet transform-artificial neural network modeling were the best, R2 of modeling and verification were 0.93 and 0.90 at jointing stage, 0.93 and 0.90 respectively at booting stage, and 0.93 and 0.90 respectively at flowering stage, 0.86 and 0.85 at full growth stage respectively; at the filling stage, the results of fractional differential-artificial neural network were the best, R2 of modeling and verification were 0.97 and 0.90, respectively, which provided technical scheme for remote sensing estimation of crop chlorophyll content.