The existing image feature matching methods still have significant limitations in dealing with complex scenes such as lighting changes and geometric deformations, due to the lack of depth information and global constraints in the measurement of feature matching. A method for measuring light field feature matching guided by disparity information is proposed to address this issue. Applying Fourier disparity layer decomposition to light field data to construct a scale disparity space, in order to extract light field features containing disparity information. A feature matching metric model that relies on depth cues of light fields was constructed based on projection transformation relationship models of light field features from different perspectives. The method of using artificial neural networks to learn the parameters of the projection transformation model aims to minimize the reprojection error as the objective function, and uses iterative optimization to achieve high-precision solution of the optimal projection transformation model, ultimately achieving the accuracy of feature point matching. The experimental results on the light field feature matching dataset show that compared to existing mainstream feature matching methods, the proposed disparity-guided light field feature matching metric model achieves better matching accuracy and robustness for scenes with lighting changes, geometric deformations, non Lambertian reflective surfaces, repetitive textures, and significant depth changes.