In order to improve the real-time performance in situation awareness of intelligent optical fiber composite overhead line, the artificial neural network (ANN) method is introduced into the strain demodulation along the optical fiber. The structure of the ANN for strain demodulation is determined. The programs of the least-squares spectrum fitting method and ANN method based on Lorentzian model are written. The ANN is trained by the Brillouin spectra with different signal-to-noise ratios (SNRs) and Brillouin frequency shifts. The trained ANN is applied to the optical fiber strain measurement for an optical fiber composite overhead line. The results of the two methods are compared from different aspects. The results indicate that the ANN method can effectively obtain the Brillouin frequency shift along the optical fiber and then obtain the strain, which has the similar accuracy as the spectral fitting method. However, its computation time is much less than that of the spectrum fitting method. The work provides a reference value for improving the real-time performance of situation awareness of intelligent optical fiber composite overhead lines.