In the previous study of the estimation model of soil organic matter content, most models were based on the feature bands, linear and non-linear empirical models rarely explored the ability promotion using an extended modeling dataset. To further improve the performance of the estimation model, it proposed a dynamic estimation model of soil organic matter content using generative adversarial networks (GAN) to generate the pseudo hyperspectral and organic matter content. Paddy soil samples and hyperspectral data (350~2 500 nm) were collected from Changsha and its surrounding areas of Hunan Province, and the organic matter content was monitored chemically. Based on these data, equivalent new samples were generated by GAN and combined with the modeling set to form anenhanced modeling set. After completing each epochformal training of GAN, the prediction models of soil organic matter content were dynamically constructed using cross-validation ridge regression (RCV), partial least squares regression(PLSR) and BP neural network (BPNN) on four observation points (corresponding 50, 100, 150 and 239 generated samples in enhanced modeling set) (the abbreviation of models were GAN-RCV, GAN-PLSR and GAN-BPNN). The experimental results showed that: (1) Among the estimation models fitted on modeling set of the origin data, RCV was the best-performing model, whose determination coefficient (R2) and root square error (RMSE) were 0.831 1 and 0.189 6; (2) In the 150 epochs formal training of GAN, the performance of GAN-RCV, GAN-PLSR and GAN-BPNN dynamically constructed on the enhanced modeling set were significantly improved, specific performances: R2 of GAN-RCV obtained the maximum 0.890 9 (RMSE 0.153 7), minimum 0.850 5 (RMSE 0.18) and mean 0.868 7 (RMSE 0.168 6), the maximum R2 increased by 7.2% (RMSE decreased by 18.9%) compared with RCV fitted on the modeling dataset, R2 of GAN-PLSR had the maximum 0.855 4 (RMSE 0.176 9), minimum 0.727 0 (RMSE 0.243 2) and mean 0.780 1 (RMSE 0.217 7), the maximum R2 increased by 20.6% (RMSE decreased by 29.5%) than PLSR constructed on the modeling dataset, GAN-BPNN performed best, whose R2 had the maximum 0.905 2(RMSE 0.143 3), minimum 0.801 7(RMSE 0.207 3) and mean 0.868 1(RMSE 0.168 6), the maximum R2 increased by 30.8%(RMSE decreased by 44.5%) comparing BPNN fitted on the modeling set; (3) With the increase of the number of generated samples in the enhanced modeling dataset, the improvement effect of model accuracy showed a trend of increasing first and then decreasing, and among the four observation points, the model constructed on the third had the most significant performance improvement. Sufficient experiments showed that the dynamic estimation model based on GAN improved the performance effectively. According to the evaluation results on the test set, the optimum model could be used to predict the soil organic matter content in the follow-up application.