The growing demand for immersive experiences has significantly influenced research in the quality assessment of light field images (LFIs). However, LFIs are susceptible to distortion during encoding, transmission, and compression, making accurate distortion measurement a current concern. In the full-reference (FR) quality assessment of LFIs, the discrepancy between the distorted and reference images is typically learned for network acquisition, overlooking the potential to enhance learning efficiency and accuracy by incorporating error map input. To address this, we propose a framework for the quality evaluation of FR LFIs based on multi-feature interactive fusion. The framework comprises three components: feature encoding network, spatial angle interactive fusion network, and score regression network, aimed at obtaining the quality score of LFIs. The feature encoding network leverages different extraction networks to capture spatial, angular, and error information, enabling the network to focus on key areas. In the spatial angle interactive fusion network, the feature fusion network integrates features from different networks, enriching and unifying information, the spatial angle interactive network further extracts distortion information from the enriched feature maps. The resulting framework demonstrates strong subjective and objective agreement in the quality assessment of LFIs, offering theoretical and practical implications for algorithm optimization and application.