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In order to measure soil organic matter content quickly and accurately, a method based on artificial olfactory was proposed. Firstly, the response curves of soil gas were collected by an array composed of 10 gas sensors controlled at different temperatures. And then seven features, including the maximum value, minimum value, mean value, mean differential coefficient value, response area value, transient value at the 30th second and transient value at the 60th second were extracted from each sensor response curves to build an olfactory feature space. Finally, the prediction model was established by using the regression algorithm. To reduce the influence of different regression algorithms, abnormal samples and redundancy characteristics on the prediction performance of the model, the Monte Carlo sampling (MCS) method was used to eliminate abnormal samples, and the principal component analysis (PCA) method was used to reduce the dimension of olfactory feature space. Moreover, three modeling methods, including partial least square regression (PLSR), support vector machine regression (SVR) and back propagation neural network (BPNN), were used to predict soil organic matter content. And the predictive performance of each model were evaluated by coefficient of determination (R2), root mean square error (RMSE) and ratio of prediction derivation (RPD). The results showed that the R2 values of PLSR, SVR and BPNN were 0.86, 0.91 and 0.85, respectively;the RMSE values were 2.49g/kg, 2.05g/kg and 2.68g/kg, respectively;and the RPD values were 2.49, 3.02 and 2.32, respectively. The prediction performance of SVR model was higher than that of PLSR model and BPNN model, which can accurately predict the organic matter content. The results can provide a reference method for the prediction of soil organic matter. 