The invention discloses a satellite observation completion method based on reanalysis data and unbalanced learning, and the method comprises the steps of proposing an R2S framework, employing relatedvariables in the reanalysis data to simulate satellite observation, thereby filling the blank of satellite observation, constructing an R-S data set through employing an STM method under the R2S framework, and obtaining a satellite observation completion model suitable for tropical cyclone sea surface wind speed, wherein the R2S framework can significantly improve the space coverage rate and timeresolution of satellite observation; the invention further provides an SIMBA method, the performance of the complementation model at the high wind speed is improved through unbalanced learning, the method is combined with conventional machine learning to obtain a hybrid complementation model; the hybrid model is superior to the conventional machine learning model in the aspect of high wind speed complementation and superior to the unbalanced learning model in the aspect of medium-low wind speed complementation, and the completion result of the hybrid model is close to the field observation value, and the completion result is accurate.