Fenitrothion, an organophosphate insecticide widely appeared in agricultural crop cultivation, is commonly used to prevent and control insect pests in maize. However, excessive or unreasonable application lead to the accumulation of pesticide residues in maize, which concern to agricultural products safety and human health. The routine methods for fenitrothion detection are chromatography-mass spectrometry and high performance liquid chromatography, which are both highly accurate. Nevertheless, the shortcoming of above methods is that they need well-trained personnel, complicated sample preparation, considerable detection time. Surface-enhanced Raman spectroscopy (SERS) has the advantages of rapid speed, high sensitivity, excellent specificity, and extensively applied for rapid detection of trace residues in agricultural products. In this paper, an accurate methodology for detection of fenitrothion residues in maize was developed using surface enhanced Raman spectroscopy and chemometric methods. The gold nanorods solution synthesized by the two-step seed-mediated growth method was used as Raman active substrate. And SERS spectra of 600 to 1 800 cm-1 were measured. Comparing SERS spectrum of ethanol solution with fenitrothion and gold nanorods, the characteristic peaks of fenitrothion were determined at 650, 830, 1 082, 1 241, 1 344 and 1 581 cm-1. A simple pretreatment method was developed for extraction of fenitrothion residues in maize. Maize contaminated with fenitrothion was grinded, and then ethanol solution was added to extract fenitrothion residues twice. Next, the two extraction were centrifuged and the supernatant were acquired, followed by mixed, concentrated and evaporated in water bath. The concentrated supernatant was used for SERS measurement. Fifty samples were prepared for each concentration of fenitrothion residues in maize. Reference value of residue in extraction solution was detected by gas chromatography-mass spectrometer. Through observing the spectrum of maize extraction with fenitrothion residues, the characteristic peak intensity of 1 082, 1 241 and 1 581 cm-1 were rapidly weakened or even disappeared as the fenitrothion residues decreased in different concentration residues extraction whereas the peak at 650, 830 and 1 344 cm-1 remained visible with fenitrothion of 0.48 μg·mL-1. Spectra of extraction with 0.37 μg·mL-1 fenitrothion residues were basically consistent with uncontaminated samples extraction. Principal component analysis (PCA) was adopted to extract the main information of spectra of fenitrothion residues. The principal component scores for spectra of 0.37 μg·mL-1 fenitrothion residues and uncontaminated samples were overlapped in scatter plot while others were distributed in different positions. It can be further determined from the scatter plot that the detection limit of fenitrothion in maize could reach 0.48 μg·mL-1, which is lower than the maximum residue limit of China in crops, suggests SERS is of high sensitivity. The intensity variation of characteristic peak of 650, 830 and 1 344 cm-1 in 50 samples with a concentration of 14.25 μg·mL-1 fenitrothion residues were analyzed, and the collected spectra showed a good repeatability while the relative standard deviation (RSD) was only 3.12%. Support vector machine regression (SVR) was employed for quantitative analysis of fenitrothion residue. Additionally, Savitzky-Golay convolution smoothing and wavelet transform (WT)were used for the pretreatment of spectral data. The calibration and prediction set of samples were divided by Kennard-Stone algorithm. Quantitative evaluation of model performance was based on root mean square error of correction (RMSEC), coefficient of determination of correction (R2ｃ), root mean square error of prediction (RMSEP) and coefficient of determination of prediction (R2p). Optimal regression model, which has minimal prediction error, was developed by SVR and WT. The correction set of RMSEC and R2c were 0.103 2 μg·mL-1 and 0.999 74 while the prediction set of RMSEP and R2p were 0.134 1 μg·mL-1 and 0.999 60 respectively. Furthermore, the predicted value of optimal model was basically in consonance with GC-MS, and predicted recovery of fenitrothion residues in maize was 95.31%~100.66%. Results demonstrates that SERS combined with chemometric method is feasible to detect fenitrothion residues in maize. This method is expected to be generalized to detect varieties of pesticide residues in other crops, providing a novel approach for the safety detection of agricultural products.