Potassium (K) is one of the three nutrient elements of honey pomelo, which is an important index for accurate diagnosis and quantitative evaluation of growth status. The hyperspectral estimation model of K contents in honey pomelo leaves was established to provide basis for rapid, non-destructive and accurate estimation of K contents. Based on the hyperspectral data of pomelo leaves and the measured data of K contents, this study first analyzed the correlation between the K contents of pomelo leaves and the original and first-order differential spectra, then analyzed the correlation between sensitive band vegetation index and the K contents of pomelo leaves, and found out the spectral parameters with good correlation with the K contents of pomelo leaves, then the partial least squares regression model (PLS), BP neural network regression model (BPNN), random forest regression model (RF) and support vector machine regression model (SVM) of pomelo leaves K contents were established, and the best estimation model of K contents in pomelo leaves was determined. In the measured band of 350-1050 nm, the spectral reflectance of pomelo leaves decreased with the increase of K contents. In 513-598 nm and 699-735 nm, it reached a significant negative correlation level, the maximum negative correlation coefficient was -0.47 (554 nm) and -0.45 (715 nm), respectively. In 507-552 nm and 691-711 nm, potassium in pomelo leaves reached a significant negative correlation level with the first-order spectral reflectance, the maximum negative correlation coefficient was -0.54 (528 nm) and -0.53 (702 nm). In 557-655 nm, it reached a significant positive correlation level, and the maximum positive correlation coefficient was 0.58 (579 nm). 554, 715, 528, 579, 702 nm were selected to construct the spectral parameters and establish the difference vegetation index (DVIλ1, λ2), ratio vegetation index (RVI λ1, λ2) and normalized difference vegetation index (NDVI)λ1, λ2). NDVI°579,702, RVI554,715, RVI°528,579, R°579 were the spectral parameters that had good correlations with the K contents of pomelo leaves. Four regression models such as PLS, BPNN, RF and SVM were established to estimate K content in pomelo leaves and verified. R2, RMSE and RE of the estimation model of pomelo leaves K contents established by RF method was 0.84, 1.49 and 7.84%, respectively. R2, RMSE and RE of the estimation model by SVM method were 0.81, 1.61 and 8.01% respectively. While R2, RMSE and RE of the estimation model by BPNN method were 0.74, 2.28 and 9.01% respectively; R2, RMSE and RE of the estimation model by PLS method were 0.72, 2.44 and 9.95% respectively. R2 of the validation model of PLS, BPNN, RF and SVM methods were 0.79, 0.84, 0.85 and 0.82 respectively. Compared with PLS, BPNN and SVM, RF had higher R2, lower RMSE and lower RE, indicating that the accuracy of RF based K contents estimation model was higher than that of PLS, BPNN and SVM. Through the comparison of four hyperspectral estimation models for K contents in Guanxi honey pomelo leaves, the accuracy of random forest estimation model was higher than that of PLS, BPNN and SVM.