To address the issue of decreased tracking accuracy due to variation in illumination in hyperspectral target tracking tasks, a hyperspectral target tracking algorithm based on deep spectral-ternary concatenated (DSTC) features is proposed. A threshold can be initially set to segment the target from the background by utilizing the local spectral curve of the target. The spectral curve of the target can be captured by utilizing band matching, and the spectral weight curve of the target can be derived by computing the structural tensor. Subsequently, dimensionality reduction of the hyperspectral image can be accomplished by performing spectral angle distance operation between the spectral curve of the target and hyperspectral image. Hence, the deep features of the target can be extracted. The scale invariant local ternary pattern (SILTP) features are extracted from the target image. Then, the spectral weights are allocated to SILTP features, and SILTP features are integrated with spectral information to derive the spectral-ternary concatenated (STC) features. The dimension-reduced target deep features and STC features are convolved by channels to obtain more discriminative and robust DSTC fusion features. Finally, the fused DSTC features are fed into the dual correlation filter. The experimental results demonstrate that the tracking algorithm proposed in this study exhibits superior tracking performance under the challenge of illumination variations when compared to the current state-of-the-art algorithms.