The luminance dynamic range of real scenes can reach up to nine orders, whereas the existing light field camera captures light field images (LFIs) with a limited luminance dynamic range of about two orders. Although LFIs can be enhanced using multi-exposure fusion (MEF) technologies, this process introduces various distortions that affect human visual perception. Therefore, it is crucial to develop an effective quality assessment tool for multi-exposure fusion light field images (MEF-LFIs). Starting from the complex distortion characteristics generated by MEF with dynamic scenes, this paper proposes an MEF-LFI quality metric with dynamic region segmentation. The proposed metric consists of three main modules. The first module focuses on spatial feature extraction and incorporates a dynamic region segmentation scheme to address artifacts in MEF-LFIs. The second module utilizes Shearlet transform for spectrum feature extraction to characterize detail loss. Additionally, the third module addresses distortion in the angular domain of MEF-LFIs through angular feature extraction using epipolar plane images and tensor decomposition. Finally, the quality of the MEF-LFIs is evaluated by integrating spatial features, spectrum features and angular features into feature vectors which are then inputted into a regression model. The experimental results show that the proposed metric is superior to the representative quality metrics and has better consistency with the human visual perception.