Assessing hunger levels is a critical step in achieving precise feeding in aquaculture. Existing feeding methods often rely on manual experience， lack a basis for determining feeding amounts， and lead to overfeeding. Current common classifications of fish feeding behavior mostly depend on the intensity of water splashing during feeding to qualitatively analyze feeding intensity. However， these classification criteria are highly subjective and essentially still rely on manual experience. In aquaculture， approximately 15%-30% of feed is wasted due to overfeeding， with overfeeding contributing 12.3% of this waste.Therefore， a quantitative detection method based on the fusion of depth video and infrared video is proposed to improve the accurate determination of bass feeding intensity. The processed frame images from depth and infrared videos are input into the DAIF-MOG2 （Depth and Infrared Fusion-Mixture of Gaussians 2） optimized model for feature extraction. The extracted results are then fused and evaluated for hunger levels， ultimately providing a quantitative hunger score. For the DAIF-MOG2 optimized model， improvements were made based on the MOG2 algorithm， transforming single-stage， single-modal learning into a multi-stage， multi-modal learning approach. This enhances learning stability and compensates for the limitations of a single modality. The introduction of shape feature constraints and physical space validation constraints improves overall detection performance and robustness in complex environments. Experimental results demonstrate that the proposed multimodal fusion model for quantifying bass feeding intensity achieves an accuracy rate of 94.2%. Compared to the original MOG2 model， its overall performance improves by 51.6%. It enables rapid assessment of fish hunger levels， effectively utilizes multimodal information， ensures timely and accurate feeding of bass in practical aquaculture scenarios， and reduces feed wastage.