Photoacoustic spectroscopy (PAS) technology is very useful in online detection of dissolved gases in transformer oil to ensure the safety of transformers. However, in the outdoor environment, the measured background signal of the instrument is easily affected by the ambient temperature and humidity, and the correction of these factors must be considered to improve the stability and reliability of the online measurement of PAS instrument. The background correction factor is generally obtained by regressionof different humidity and temperature measured in the laboratory, but the environment often changes during measurement procedure, so a robust regression algorithm is needed to obtain accurate correction factor. The temperature and humidity correction method based on the support vector regression is studied in this work. Acetylene is selected as the research object. Humidity air with different concentrations is generated by using humidity generator in laboratory,and the temperature of the PAS cell is measured by temperature sensor, and then the background signal correction factor of acetylene is regressed from the measured data. Furthermore, the method is verified by acetylene air mixture with acetylene volume fraction of 0, 5×10-6 and 2×10-5. The results show that the proposed method and the least squares regression method have the same trend for acetylene mixture with volume fraction of 5×10-6 and 2×10-5. Whereas the least squares regression has tendency in residuals, and the proposed method has better repeatability and stability for temperature and humidity correction than the least square method.