###### Abstract  
Wood dyeing plays a crucial role in enhancing the value of plantation wood and addressing the imbalance between supply and demand in the wood industry. However, challenges such as low dye uptake and inaccurate color matching persist. This study introduces a novel Separable Attention Feature Fusion-Gated Bidirectional Long Short-Term Memory (SAT Fusion-Gated BiLSTM) model to more accurately predict dye concentration and ultrasonic pretreatment power to address these issues. The model integrates multispectral data and uses ultrasonic treatment to open fiber gaps and decompose wood components, thereby improving dye uptake. Unlike traditional models, SAT Fusion-Gated BiLSTM combines a Maximum Information Coefficient (MIC)-based feature selection module, gated linear units for enhanced feature extraction, and an improved cross-attention mechanism for efficient data fusion. Compared to traditional deep learning models, the model's performance is significantly improved, including an \(R^{2}\) of 0.9274, an \(RMSE\) of 0.0470, an \(RPIQ\) of 5.5825, an \(RPD\) of 5.0400, and a \(MAE\) of 0.0229. \(RPIQ\) and \(RPD\) improve by over 100.8% compared to traditional deep learning models, and the runtime is reduced by 83.94% when compared to Convolutional Neural Networks (CNNS). The Shapley Additive Explanation (SHAP) reveals the key spectral features that drive these predictions. These advancements in accuracy, efficiency, and interpretability make the model particularly promising for industrial applications, offering both improved operational efficiency and more precise resource management in the sustainable use of timber. v 2022 2021 20  
can rupture weak structures, such as perforated membranes and parenchyma tissues, creating new fluid pathways and enhancing wood permeability [2; 3; 4; 5; 6], thereby improving the staining performance. Ultrasonic technology has seen widespread application in wood research, particularly for increasing wood permeability. [PERSON] et al. [7] conducted an ultrasonic pretreatment on eucalyptus wood and found that ultrasonic waves help remove extractives from internal pores and create microchannels within the cell walls, further enhancing permeability. Furthermore, the elevated hydroxyl content in the pretreated samples can facilitate the formation of additional reaction sites for reactive dye molecules, thereby accelerating the dyeing process. Although research specifically focused on the effects of ultrasonic pretreatment on staining performance is limited, studies on ultrasonic-assisted dyeing [1; 2; 4] suggest that ultrasonic pretreatment can significantly enhance staining results. Exploring the impact of ultrasonic pretreatment on staining performance and identifying the optimal pretreatment power can further optimize the staining process, reduce material waste, and offer theoretical and technical support for industrial applications. In the wood dyeing process, most formulations are traditionally determined by color matching personnel through extensive experimentation, leading to significant time and labor resource wastage [8]. To address these challenges, computer-aided color matching technologies have been applied to predict dye formulations [9]. [PERSON] et al. [10] utilized Particle Swarm Optimization (PSO) to enhance the Friele and Stearns-Noechel models, significantly improving the efficiency and accuracy of dye formulation predictions. However, the numerous assumptions inherent in traditional color matching theories often compromise the accuracy of the results. Consequently, recent years have seen a growing application of machine learning methods in predicting wood dye formulations, greatly enhancing the speed and precision of these predictions. [PERSON] developed an intelligent color-matching model for wood dyeing based on an improved Radial Basis Function (RBF) neural network, achieving faster convergence and higher accuracy compared to traditional RBF networks [11].