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Objective A new energy conversion method based on support vector regression has been put forward to enhance the function of former attenuation correction process.Support vector regression based PET/CT image attenuation correction method is used to make up the shortcomings in terms of adopting linear relation to fit nonlinear relation when using bilinear transformation method in the energy conversion process of electron emission or PET/CT image attenuation correction and find the best conversion relationship between CT value and linear attenuation coefficient that under the energy condition of 511 keV.Methods The simulation program called GATE (Geant4 Application Tomographic Emission) was adopted in this work to simulate 11 groups of cylinder phantoms in different kinds of materials.According to the data of the simulated cylinder phantoms,the CT values and the linear attenuation coefficients of them could be calculated and then get trained through SVR model to set up new models.Finally,the new method and the former one were both applied to the GATE stimulated NCAT (NURBs Cardiac-Torso) pixel phantom image analysis process to evaluate the accuracy of them separately.Results The results showed that the calculated relative error percentages of the new method were smaller under the condition of 511keV using PET/CT image attenuation correction method (i.e.the relative error percentages for the data of the lung and liver were 3.1% and 1.08%).Additionally,the corrected PET images could have the minimum MSE value (176.9230) ，the maximum PSNG and AG value (31.8621 and 7.9083).The PET images corrected by the new method were more similar to the static images in comparison with those using former process.Conclusions The performance of the support vector regression based PET/CT image attenuation correction method is better than the previous one.The new method is more consistent with the transformational relations between CT value and the linear attenuation coefficient under the energy condition of 511keV,hence it could have better accuracy in analyzing PET/CT images quantitatively therefore enhancing the precision in clinical diagnosis as well.