SO₂ UV camera has been successfully applied in volcanic activity monitoring and its dynamics research due to its remarkable advantages in temporal resolution, spatial resolution, detection sensitivity, and detection accuracy. To address the issues that SO₂ emission rate retrieved from UV camera images is easily affected by plume turbulence and the imges obtined are often with low contras, an optical flow algorithm incorporating neural network is proposed in this work. Firstly, based on the characteristics of atmospheric ultraviolet radiation transmission, the working mechanism of the SO₂ UV camera and the inversion method of SO₂ concentration image are described. Secondly, the neural network is integrated into the optical flow algorithm to achieve accurate inversion of SO₂ emission rate in volcanic plume images; Finally, compared with the traditional optical flow methods, the superiority and accuracy of the proposed neural network optical flow algorithm is confirmed. The experimental results show that the neural network optical flow method can reduce the error of edge inversion from 94% to 5% even under the dual influence of low contrast of images and plume turbulence effect, significantly improving the accuracy of SO₂ emission rate inversion.