It is difficult to rapidly and accurately detect the component content in the praseodymium/neodymium (Pr/Nd) extraction process. This paper proposes a multi-RBF model and its adaptive correction method of component content. Extracting the HSI image features of Pr/Nd mixed solution

the first moments of H and S components are selected as input variables. Using the subtractive clustering algorithm

the sample data are divided into several categories and the corresponding sub-models are obtained based on RBF neural network. To further realize the high-accuracy prediction of the element component content

a parameters adjustment strategy is designed to automatically adjust the network structure and parameters of sub-models when the change of operating environment or the object characteristics results in the accuracy of the prediction model doesn't meet control requirements. The comparison experiments on actual production data from Pr/Nd extraction process show that the proposed method can meet the high-accuracy and rapid requirements of element component content detection in rare earth extraction process.