Abstract: Self-adaptive evolutionary extreme learning machine (SaE-ELM) is a type of single hidden layer feedforward neural network learning algorithm with adaptive differential evolution algorithm to optimize the hidden node parameters. Monitoring key parameters during bulk flue-curing of tobacco is difficult, therefore the dynamic variations of leaf moisture content, chlorophyll (SPAD) and starch in tobacco leaves were monitored by combining near infrared spectroscopy with SaE-ELM and adopting cross validation to select the number of hidden layer nodes. The results showed that comparing with partial least squares (PLS) regression, BP neural network, support vector machine (SVM) regression and extreme learning machine (ELM) quantitative models, SaE-ELM models for moisture, chlorophyll and starch contents had the advantages of automatic parameter optimization, better performance, stronger generalization ability and accurate prediction results, achieving correlation coefficients of 0.931 2, 0.917 6 and 0.916 7, respectively. Near infrared technology combined with SaE-ELM can thus accurately determine the variations of these key parameters during bulk flue-curing of tobacco, which provides technical reference for controlling tobacco bulk curing.