###### Abstract
By integrating spectral similarity metrics before and after dyeing with the spectral features of the dyed veneer, this approach captures more detailed spectral variations, providing a more accurate representation of optical changes due to the dyeing process. Traditional feature concatenation methods, however, often overlook intrinsic correlations between features, leading to redundancy and diminished prediction performance. In multimodal learning, cross-modal attention enables models to selectively focus on relevant information from multiple data types during training. [PERSON] et al. [28] proposed a multimodal cross-attention network that models both intra- and inter-modal relationships between image regions and sentence words, effectively capturing fine-grained visual-semantic correlations to enhance image-sentence matching. [PERSON] et al. [29] introduced the Cross-Modality Attention Transformer (CAT), which extracts complementary information from visible and thermal image pairs, improving multimodal pedestrian detection. [PERSON] et al. [30] developed a Cross-Modal Attention (CMA) module that fuses red, green, blue (RGB) and depth features for 6D object pose estimation, improving accuracy and robustness. These studies underscore the significance of cross-modal attention in multimodal learning and its ability to integrate diverse modalities. However, its application to intelligent wood dyeing formula prediction remains largely unexplored. This study proposes a novel feature fusion module using cross-modal attention to integrate spectral features, thereby enhancing prediction accuracy. To enhance the dyeing performance of wood and leverage the advantages of multimodal learning, this study investigates the dyeing of Pinus sylvestris veneers after ultrasonic pretreatment. The impact of ultrasonic pretreatment on dyeing performance is examined, and a deep learning model based on the Improved Dung Beetle Optimized Separable Attention Feature Fusion-Gated Bidirectional Long Short-Term Memory (IDBO-SAT Fusion-Gated BiLSTM) network is proposed to predict dye formulations and ultrasonic pretreatment power. The main contributions are as follows:  
1. Incorporation of spectral similarity metric parameters and characteristic parameters: the study integrates spectral similarity metric parameters and characteristic parameters into the prediction of wood dyeing formulations and ultrasonic pretreatment power. Feature selection based on the MIC combines selected features with spectral reflectance, enabling a richer representation of the spectral properties of dyed veneers. 2 Development of a multispectral predictive model: a model based on multispectral data is proposed, featuring a gated linear unit for intra-modal feature extraction, a Separable Attention Feature Fusion (SAT Fusion) module for integrating diverse spectral data, and a gated BiLSTM unit for prediction. This architecture facilitates deep interaction among spectral data, improving training efficiency, stability, and handling of unaligned spectral sequences. 3 Optimization using an Improved Dung Beetle (IDBO) algorithm: the SAT Fusion-Gated BiLSTM model is optimized with an improved dung beetle algorithm, significantly enhancing the accuracy of predictions for dye formulations and ultrasonic pretreatment power. 4 SHAP-based spectral feature analysis: spectral features are analyzed using the SHAP algorithm, identifying the most influential spectral features in the model and their impact on dye concentration and ultrasonic pretreatment power predictions. The remainder of this paper is organized as follows. Section 2 outlines the SAT Fusion-Gated BiLSTM prediction model and the innovative methods proposed. Section 3 presents an in-depth discussion of the SAT Fusion-Gated BiLSTM model optimized with the improved dung beetle algorithm. Section 4 examines the effects of ultrasonic pretreatment on dyeing performance and compares the proposed model with traditional methods in predicting ultrasonic power and dye formulations. Finally, Section 5 concludes the study.