The　rising　popularity　of　light　field　imaging　underscores　the　pivotal　role　of　image　quality　in　user　experience.　However,　evaluating　the　quality　of　light　field　images　presents　significant　challenges　owing　to　their　highdimensional　nature.　Current　quality　assessment　methods　for　light　field　images　predominantly　rely　on　machine　learning　or　statistical　analysis,　often　overlooking　the　interdependence　among　pixels.　To　overcome　this　limitation,　we　propose　an　innovative　approach　that　employs　a　universal　backbone　network　and　introduces　a　dual-task　framework　for　feature　extraction.　Specifically,　we　integrate　a　staged　＂primary-secondary＂　hierarchical　evaluation　mode　into　the　universal　backbone　networks,　enabling　accurate　quality　score　inference　while　preserving　the　intrinsic　information　of　the　original　image.　Our　proposed　approach　reduces　inference　time　by　over　75%　compared　to　existing　methods,　simultaneously　achieving　state-of-the-art　results　in　terms　of　evaluation　metrics.　By　harnessing　the　efficiency　of　neural　networks,　our　framework　offers　an　effective　solution　for　the　quality　assessment　of　light　field　images,　providing　superior　accuracy　and　speed　compared　to　current　methodologies.