It is difficult for consumers to distinguish regional rice brands formed by geographical factors instead of rice of similar producing areas. Based on Raman spectroscopy, the experiment compared three common pretreatment methods including first derivative+translational smoothing, second derivative+translational smoothing, wavelet transform+baseline removal. In addition, an improved piecewise polynomial fitting+baseline removal was proposed, and a total of four pretreatment methods respectively were combined with partial least square method to identify rice of similar origin, and an optimal pretreatment method for identifying rice of similar origin was proposed. Firstly, 150 rice spectral samples with a Raman displacement of 200~3 100 cm^-1 were collected by Raman spectrometer from three similar producing areas in an county, Heilongjiang province. Then, the original Raman spectra were preprocessed by first derivative + translational smoothing, second derivative+translational smoothing, wavelet transform+baseline removal, and piecewise polynomial fitting + baseline removal. A total of 99 samples were selected from 33 samples from each origin for training, and a partial least square analysis model based on partial least square method was established for the unknown 51 samples. In the training set, the preprocessing method of first derivative+translation smoothing had the maximum correlation coefficient value, the minimum mean square error and the minimum root mean square error. Andwavelet transform+baseline removal hadthe minimum correlation coefficient value, the maximum mean square error and the maximum root mean square error. In the test set, the preprocessing method of 3 points and 2 times fitting+baseline removal had the maximum value of correlation coefficient, the minimum mean square error and the minimum root mean square error, and the preprocessing method of the second derivative+translation smoothing was the worst. Finally, the PLS modeling results showed that, in the training set, the correct discrimination rate of rice from three producing areas was 100% by using four kinds and nine pretreatment methods. In the test set, the total recognition rate of rice from three producing areas was 100% by using 3-point 2-time fitting+baseline removal, and 52.9% by using 5-point 2-time fitting + removing baseline. Other piecewise polynomial fitting was between the two. The total recognition rates of the first derivative + translation smoothing, the second derivative + translation smoothing and the wavelet transform were 88.2%, 86.2% and 96.1%, respectively. It is found that the preprocessing method of 3-point 2-time fitting + removing baseline in piecewise polynomial fitting has obvious advantages and is consistent with the results of the correlation coefficient, mean square error and root mean square error, with high overall recognition rate and stable identification effect.