Taking the Lingbei rare earth mining area in Dingnan county of Jiangxi Province as the research object of the reclaimed vegetation, the original spectrum, derivative spectrum and the continuum removed spectrum of the reclaimed vegetation were detected. The spectral characteristics and variation regularity of the typical reclaimed vegetation were analyzed, the correlation between chlorophyll content and spectral characteristic index of reclaimed vegetation was analyzed, and the sensitive spectral parameters were extracted. Partial Least Squares Algorithm, Back Propagation Neural Network Algorithm and Sparse Autoencoder Network Algorithm were selected to construct the estimation model of chlorophyll content, and compare the accuracy. The results show that; The vegetation spectrum of rare earth mine reclamation has the spectral characteristics of higher reflectance in visible region, red shift of green peak and red valley, blue shift of “red edge”, with less spectral variation in bamboo willow; Variability in the sensitive spectral parameters extracted from different vegetation; Sparse Autoencoder network algorithm is the optimal estimation model (R2 value of three vegetation is 0.9117,0.7418 and 0.9815 respectively). In the case of the small sample, it has higher estimation precision and universality for different reclaimed vegetation.