Hyperspectral videos contain a larger number of spectral bands, providing extensive spectral information and material identification capabilities. This advantage confers hyperspectral trackers to achieve superior performance in challenging tracking scenarios. However, the limited availability of hyperspectral training data and the inability of existing algorithms to fully exploit hyperspectral information restrict the tracking performance. To address this issue, a novel framework, Spectral Prompt-based Hyperspectral Object Tracking (SP-HST), is proposed. SP-HST leverages a RGB tracking network as the main branch for feature extraction and tracking, which accounts for more than 98% of the total parameters and remains frozen during the training procedure. Additionally, the Spectral Prompt Learning (SPL) branch, comprising multiple lightweight prompt blocks, is introduced to generate complementary spectral representations as the prompt. The prompts contain abundant spectral information from hyperspectral data, enhancing the discriminative ability of features within the main branch. Furthermore, the Complementary Weight Learning (CWL) is employed to calculate the importance of spectral information from different prompts, enabling the features for hyperspectral object tracking to contain more spectral information that is absent in the feature of the main branch. By utilizing the spectral information as prompt, the number of trainable parameters is less than 2% of that in the tracking network, and the convergence is reached in 12 training epoch. Extensive experiments demonstrate the superiority of SP-HST, achieving a new state-of-the-art tracking performance, 71.3% of the AUC score on the HOTC dataset and 96.7% of the DP@20P score on the IMEC25 dataset. The code will be released at https://github.com/lgao001/SP-HST.