###### Abstract
As machine learning techniques have advanced, deep learning has emerged as a powerful tool across many research fields, particularly in sequence data prediction. Studies indicate that the deep architectures of neural networks are particularly well-suited for sequence data, outperforming traditional machine learning models [12]. Long Short-Term Memory (LSTM) networks [13; 14] and Bidirectional Long Short-Term Memory (BiLSTM) [15; 16] have demonstrated a strong performance in capturing nonlinear relationships within sequence data, making them ideal for wood dye formulation prediction based on spectral data. However, these models involve a large number of trainable parameters, requiring extensive tuning, which is both time-consuming and labor-intensive [17]. Consequently, a variety of optimization algorithms have been employed to automate the search for optimal network parameters [18]. Despite their superior ability to process high-dimensional data [19], deep learning models face challenges due to their \"black-box\" nature, limiting their broader application. Exploring the interpretability of these models is, therefore, essential [20]. Interpretable models can identify which features most significantly influence predictions and clarify the impact of each feature on the final output [21]. The Shapley Additive Explanation (SHAP), based on game theory [22], has become a popular method for interpreting deep learning models, and has been successfully applied in various domains. [PERSON] et al. [23] integrated Artificial Intelligence (AI)-based deep learning models with SHAP to investigate the multivariate relationships between regional imaging measurements and cognition, uncovering key brain regions associated with Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD). Similarly, [PERSON] et al. [24] incorporated SHAP into Lightweight Parallel Depthwise Separable Convolutional Neural Networks (LPDCNNs) and the Ridge Regression Extreme Learning Machine (Ridge-ELM) framework to generate interpretable image sets for lung cancer, improving both model interpretability and clinical applicability. [PERSON] et al. [25] applied SHAP to interpret a deep neural network model predicting concrete compressive strength, validating the findings against experimental data. Despite SHAP's widespread use, its application in predicting wood dye formulations remains limited. This study aims to address this gap by uncovering relationships between spectral data and dyeing parameters, thereby advancing the development of interpretable predictive models in wood dyeing research. In spectral analysis, the spectral characteristic parameters such as peak position, bandwidth, and shape index provide insights into the optical and color properties of materials. Spectral similarity metric parameters quantify variations in light intensity, color brightness, and wavelength components, offering a more nuanced understanding of material properties. Compared to traditional color difference metrics, these similarity metric parameters deliver a more precise and comprehensive measure of color variations. [PERSON] et al. [26] proposed a spectral similarity model based on the Frechet distance and Pearson correlation between spectral vectors, demonstrating superior stability and accuracy in color representation and discrimination over conventional models. Similarly, [PERSON] et al. [27] developed spectral feature fusion algorithms using parameters like the _Frechet_ distance, the correlation coefficient, the Lance distance, and the spectral angle, all of which effectively distinguish colors in color-spun woven fabrics. Current methods for predicting wood dyeing formulations rely on matching reflectance spectra but fail to account for the inherent color characteristics of the wood, neglecting pre- and post-dyeing color changes.