In this study, near infrared spectroscopy combined with chemometrics building models to was used to detect the soluble solid contents from watermelon,through four methods, K nearest neighbors regression, Random forest regression, convolution neural network, convolution neural network added residual-block. Some deep learning modules with image processing, coding modules in 1-d ways and apply in Visible/Near-infrared spectroscopy for modeling exploration was used. As a result, deep learning modules showed great potential in Visible/Near-infrared spectroscopy data processing, and CNN model got 0.855 9 correlation coefficient and 0.778 1 °Brix RMSEP in prediction-set. The Res-CNN model achieved 0.893 2 correlation coefficient and 0.710 4 °Brix RMSEP in prediction-set. The results of this study could provide a reference for the rapid and non-destructive model development of watermelon quality.