<p>This paper proposes an unsupervised wood species identification approach utilizing multiobjective optimization clustering and feature fusion. To address the inherent limitations of single-band spectra in capturing comprehensively wood characteristics, this approach integrates preprocessed low-dimensional terahertz (THz) and hyperspectral data. Additionally, to address the challenge of selecting the optimal k-value in clustering, an unsupervised wood clustering algorithm was developed, employing multiobjective optimization and evolutionary algorithms. This proposed algorithm incorporated a prototype coding method for initialization, density peak clustering for pattern identification, and an improved firefly optimization algorithm to introduce cross-variation and maintain population diversity. To further refine the clustering process, a selection operator based on grid division and fast non-dominated sorting was designed, optimizing the clustering performance. The model was evaluated on a dataset containing hyperspectral and THz spectra from 400 samples, representing ten wood species—five coniferous and five broadleaf species. Experimental results indicated that fusing the spectral data resulted in a 3.5% increase in clustering purity compared to individual datasets. Moreover, the proposed algorithm outperformed established clustering methods such as DBSCAN, OPTICS, and density peak clustering, achieving a maximum clustering purity of 91.25% across both internal and external clustering metrics. These findings demonstrate the effectiveness of the multi-spectral fusion approach and the proposed algorithm in enhancing wood species identification accuracy, offering a promising avenue for improving non-destructive evaluation methods in forestry and material sciences.</p>