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Aiming at solved the problem of failure in soft-sensing model, a multiple-model soft-sensing modeling method was proposed. Separating a whole training data several clusters with different centers by KFCM, each subset was trained by LS-SVM. In the training process, AMDE algorithm was used to optimize the parameters of the LS-SVM. The proposed algorithm is applied to the key parameters of straw fermentation, such as ethanol concentration, matrix concentration (total sugar concentration) and cell concentration detection. The predicted values obtained by soft sensor modeling are compared with off-line test values, which proves the effectiveness of the method.The experimental results show that the improved algorithm overcame the phenomenon that DE algorithm is easy to fall into the local optimum and premature convergence．Compared with the single model, the measurement errors of the ethanol concentration，the matrix concentration (total sugar concentration) and the cell density in the new model were respectively 1.54%，1.05% and 0.85%，indicating the new model can better adapt to the straw fermentation process and improve the detection accuracy．