Aiming at the problem that existing hyperspectral video tracking algorithms perform poorly when target scale variation, a hyperspectral video target tracking algorithm based on spectral dimensionality reduction and feature fusion is proposed. Firstly, the difference of the local spectral curve of the target is calculated and the spectral curve of the target is obtained by combining eigenvalue sorting and threshold setting. Subsequently, the target spectral curve and the hyperspectral image are used to calculate the spectral angular distance to achieve dimensionality reduction. Then, the improved multi-scale capsule network is used to extract multi-scale features. In order to use the information of different scales, the mask generated by dimensionalityreduction is fused with multi-scale features. Finally, the fused multi-scale features are input into the classification and regression capsule, and the template updating mechanism is used to enhance the stability and robustness of tracking, so that the algorithm can better cope with the challenges brought by scale variation. Experimental results indicate the superiority of the proposed algorithm in dealing with the challenge of scale variation.