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In order to achieve a rapid, convenient and accurate measurement of the soil organic matter (SOM) content, a multi-sensor array based on artificial olfactory technology was designed to detect SOM content. Totally ten different types of oxide semiconductor gas sensors were selected to form the sensor array, and different concentrations of hydrogen sulfide, ammonia and methane standard gases were used to test the response of the sensor array. It can be seen from the response curve that the sensor array responded to different concentrations and types of standard gases, and the response results were not the same. With the increase of the standard gas concentration, the response curve of the sensor array was also increased. The test results showed that the sensor array had high specificity and certain cross-sensitivity. And from the soil gas response curve of each sensor, six features (response area value, maximum value, average differential coefficient, variance, average value and maximum gradient) were extracted to construct an artificial olfactory feature space (AOFS). Then, partial least square regression (PLSR), back propagation neural network (BPNN) and support vector machine regression (SVR) algorithms were used to establish the prediction model of AOFS and SOM content, and the coefficient of determination (R2), root mean square error (RMSE) and absolute average error (MAE) were used to evaluate the performance of the prediction model. The test results showed that the R2 of the PLSR, BPNN, and SVR test sets were 0.80878, 0.87179 and 0.91957, the RMSE were 3.6784g/kg, 3.1614g/kg and 2.4254g/kg, and the MAE were 3.1079g/kg, 2.4154g/kg and 2.1389g/kg, respectively. The model established by the SVR algorithm had the highest R2, the smallest RMSE and MAE. It had higher predictive performance than PLSR and BPNN, and can be used for the measurement of SOM content.