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In order to reflect the input and output features of an optical fiber micro-bend sensor, a new method using general regression neural network (GRNN) to fit the characteristic curve is proposed in this paper. First, the measuring principle of optical fiber micro-bend sensor and the principle of GRNN are introduced. Then, to verify the feasibility and effectiveness of this new method, a comparison between two kinds of fitting methods is done. One is based on GRNN, the other is based on Levenberg-Marquart improved BPNN. The results of the simulation experiment show that with the same number of training samples and for small scale to medium scale networks, compared with BPNN, GRNN has smaller error, faster convergence speed and higher fitting accuracy. So the method discussed in this paper provides a reliable basis for the nonlinear compensation problem of optical fiber micro-bend sensor.