Based on the previous studies, visible near-infrared hyperspectral (350-1700 nm) data of 193 samples from sandy ginger black soil in northern Anhui province are firstly used to optimize the nine models by combing the nonlinear and linear kernel functions. Then, model combination and secondary optimization are performed via three integrated learning algorithms based on the random forest, boosting tree, and gradient boosting decision tree (GBDT). Four single models, including partial least squares of Sigmoid function, linear support vector regression, radial basis support vector regression, and support vector regression of Sigmoid function, are selected and combined by model comparison. After optimization of the integrated algorithms, it is found that the prediction results of the GBDT algorithm are optimal. The determination coefficient of the GBDT algorithm is 0.86, which is 17.8% higher than that of the single model, and the relative analysis error coefficient is 2.55, which is significantly improved from grade B to A. The GBDT algorithm not only improves the accuracy, but also has low overfitting degree and good generalization performance. Therefore, the GBDT algorithm can be combined with the advantages of multiple models and improve the accuracy of the prediction results of soil available phosphorus through hyperspectral model integration.