Soluble solids content is one of the leading evaluation indicators for internal apple quality. NIR spectroscopy is the first choice for predicting apple soluble solids. Optimizing the parameters of near-infrared spectroscopy collection devices can improve the model’s performance. In this paper, the near-infrared spectrum (350~1 150 nm) of apples was collected by the dynamic online equipment independently developed by our research group, and the effects of different parameters (movement speed, integration time, and light intensity) on the apple quality prediction model by near-infrared spectrum were studied, the parameters of the dynamic online equipment were optimized. The 210 Fuji apples were divided into two batches. The first batch of 90 apple samples was divided into a modeling set and a prediction set by the K-S algorithm, which was used to study the effect of the online prediction model on the solid soluble content of apples with different movement speeds and different integration times. At two moving speeds of 0.3 and 0.5 m·s-1, multiple scattering correction (MSC) and wavelet transform (WT) are used to preprocess the collected spectra, and the SSC model is built for the spectra with different moving speeds. The results show that the prediction model built with amoving speed of 0.5 m·s-1 performs better. Among the four different integration times, the best performance of the model built by SNV preprocessing was achieved at an integration time of 120 ms. The second batch of 120 apples was divided into modeling and prediction sets by the K-S algorithm. The influence of different light intensities on the apple’s SSC prediction model was studied using device parameters with a moving speed of 0.5 m·s-1 and integration time of 120ms. The results showed that when the light intensity was 4.5 A, the collected spectrum changed significantly compared with other light intensity groups, and the peaks at 640 and 800 nm of the spectrum disappeared. When the light intensity is 6.5A, the model after SNV pretreatment has the best performance. Competitive Adaptive Reweighting Algorithm (CARS) and Successive Projections Algorithm (SPA) were used to screen the wavelength of the collected spectral data to establish the apple SSC model. The results show that the model-based on CARS-PLS has good performance and the correlation coefficient and root mean square error of its prediction set are 0.991 and 0.149, respectively. At the same time, the model is simplified, and the stability of the model is improved. The research shows that parameter optimization of dynamic online equipment is helpful in improving the prediction accuracy of the apple model. This research is beneficial in providing technical support for online apple quality sorting.