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In view of the fact that the key parameter in the fermentation process of matsutake is difficult to be detected online，a new method of soft sensor modeling of mycelium biomass based on improved cuckoo algorithm (CS) and improved BP neural network (BPNN) is proposed. Firstly, the traditional two-phase dynamic discovery probability method is used to improve the global CS search and local search capabilities; Then the BPNN is improved by introducing additional momentum and dynamic adjustment learning rate to improve the correction accuracy of BPNN parameters; Finally, the initial weights and thresholds of BPNN are obtained by the CS algorithm, and the weights are dynamically modified by the weight correction formula (a combination of additional momentum and dynamic learning rate) to overcome the traditional BPNN soft-sensing model easy to fall into the local minimum, slow convergence and other issues. The simulation results show that the improved CS-BPNN soft-sensing model can improve the prediction accuracy of the model by more than 6%, which can meet the demand of real-time on-line measurement during the fermentation of matsutake.