In order to solve the problem of lower prediction performance caused by the difficulty in retrieving the key features from hyperspectral data of soil available potassium, this paper proposes a novel hybrid feature selection algorithm based on Random Forests. Firstly, wrapper-based feature selection methods were applied to rapidly remove the redundancies and preserve the related features. Secondly, an Improved-RF feature selection algorithm was applied to further accurately select the wavelength variables from the pre-selected feature sets. In this step, characteristic wavelength with strong robustness and discriminative could be selected through improving the dipartite degree between the key and redundant features and using an iterative feature selection method. Therefore, the problem of low prediction performance in the soil available potassium inversion model could be better solved by using our hybrid feature selection algorithm. In order to verify the validity of our algorithm, 124 representative soil samples collected from the Dagu River Basin were chosen. Using our algorithm, the optimal feature subset which contained 13 sensitive bands have been selected and used to build soil available potassium content inversion model. This work compared the model performance of full bands, current feature selection algorithms and our algorithm. The comparison results indicated that our algorithm not only selects minimum numbers of wavelength features and reduces the dimension of full bands, but also achieves better prediction performance with lower RMSEP (9.661 5), higher R (0.936 9) and RPD (2.14). As an effective method of soil available potassium inversion model, the algorithm proposed in this paper can provide theoretical basis for the design of real-time soil nutrient sensors.