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Aiming at the nonlinear output of humidity sensor, a nonlinear compensation scheme based on BP neural network is proposed. BP neural network is established based on L-M algorithm, and the input and output nonlinear compensation correction of the resistance humidity sensor is realized, Compared with the conjugate gradient algorithm and the BP neural network model proposed by the quasi Newton algorithm, the model error performance and convergence speed are compared. The results show that the BP neural network model based on L-M algorithm has more efficient performance in convergence speed, error performance and so on, its compensation correction is more suitable for nonlinear characteristics of humidity sensors.