Soluble solid contents (SSC) are an important indicator of apple quality and maturation and can be used for quality analysis and ripeness prediction. In this paper, 552 samples of Sugar Core Red Fuji apples from Aksu of Xinjiang Province were picked at equal intervals of three days from the fruit swelling and setting stage to the complete mature stage, the visible near-infrared spectroscopy (vis-NIRS) of the samples at 380 to 1 110 nm were collected respectively, and the SSC were measured. Then, the collected discrete data of vis-NIRS were transformed into spectral curves using the basis function smoothing method, i. e., function-type data, and respectively, the functional linear regression model was established with Vis-NIRS curves, first-order derivative curves, and second-order derivative curves as functional explanatory variables and SSC as scalar response variables. To confirm and analyze the performance of the model, partial least squares regression (PLSR), kernel support vector machine (KSVM), random forest (RF), gradient boosting tree (GBM) and deep neural network (DeepNN) were established by the original spectral discrete data after moving smooth, first-order derivative and second-order derivative pre-processing. The results show that among the 18 models, for the training set, the PLSR-dNIRmodel, KSVM-dNIR model, RF-dNIR model, GBM-dNIR model, and Deep NN-d2NIR model were outperformed the FunLR-NIR model, FunLR-dNIR model and FunLR-d2NIR model, and the Deep NN-d2NIR model was optimal (rc=0.999 6, R2c=0.998 6, RMSEC=0.074 0, RPDC=27.436 6). For the test set, the FunLR-NIR model, FunLR-dNIR model, and FunLR-d2NIR model outperformed all other models, and the FunLR-NIR model was optimal (rv=0.953 4, R2v=0.907, RMSEV=0.585 6, RPDV=3.301 7). The results of the training sets and test sets show that the kernel support vector machine model, random forest model, gradient boosting tree model, and deep neural network model are prone to overfitting. In contrast, the functional linear regression model has better generalizability. Besides, the prediction results of the three functional linear regression models (FunLR-NIR model, FunLR-dNIR model, and FunLR-d2NIR model) showed that all the models have good robustness and high prediction accuracy. The experimental results showed that the functional linear regression models combined with vis-NIR spectroscopy and functional data analysis could successfully and effectively achieve the prediction of soluble solid contents of apples at the ripening stage.