Aiming at the small sample problem of high sample collection repetition rate and low effective data during quality monitoring of rare earth extraction process, a method for predicting component content of rare earth extraction process based on mixed virtual sample generation is proposed. First of all, based on the small sample extracted from the field, the output data of the virtual sample are generated using the midpoint interpolation method. Then, the input data of the virtual sample are generated according to the mapping relationship between the hidden layer of the stochastic configuration network(SCN) and the output layer, the input layer and the hidden layer. In view of the limitation that the virtual sample can only be generated at neighboring points, a multi-distribution trend diffusion technology (MD-MTD) combined with the genetic algorithm (GA) is used to generate an optimized virtual sample set to supplement. According to the principle of data rationality, the virtual sample and the real small sample are merged, and a component content prediction model based on the SCN is established. Through the field data verification and comparative experimental analysis of CePr/Nd extraction, the results show that the proposed method can effectively solve the problem of the small sample, which is suitable for component content monitoring in the rare-earth extraction process.