Light field images provide users with a more comprehensive and realistic visual experience by recording information from multiple viewpoints. However, distortions introduced during the acquisition and visualization process can severely impact their visual quality. Therefore, effectively evaluating the quality of light field images is a significant challenge. This paper proposes a no-reference light field image quality assessment method based on deep learning, combining spatial-angular features and epipolar plane information. Firstly, a spatial-angular feature extraction network is constructed to capture multi-scale semantic information through multi-level connections, and a multi-scale fusion approach is employed to achieve effective dual-feature extraction. Secondly, a bidirectional epipolar plane image feature learning network is proposed to effectively assess the angular consistency of light field images. Finally, image quality scores are output through cross-feature fusion and linear regression. Comparative experimental results on three common datasets indicate that the proposed method significantly outperforms classical 2D image and light field image quality assessment methods, with a higher consistency with subjective evaluation results.