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Aiming at the problem of large detection errors caused by insufficient sample quantity in traditional fruit and vegetable quality detection methods, an apple quality detection method based on photon transmission simulation under surface light source was proposed. Taking apples as the research object, Monte Carlo method was used to simulate the motion trajectory of photons on the apple double-layer flat model, totally 20000 apple tissue surface brightness distribution maps were quickly obtained, optical parameters were used as labels, and input convolutional neural network training was used to obtain the model. The fine-tuning migration was applied to a small number of data sets of measured apple spectral images to realize the inversion of optical characteristic parameters. Finally, the output result of the fully connected layer of the network model was associated with the quality of the apple, so as to realize the nondestructive testing of the sugar content and hardness of the apple. The final result was that the inversion accuracy of pulp absorption coefficient μa2 was 93.24%, and the inversion accuracy of pulp scattering coefficient μs2 was 92.54%. The prediction accuracy of sugar content and hardness of the quality classification model were improved by 5.87 and 6.48 percentage points compared with that of the traditional method. The determination coefficient of sugar content and hardness of the quality regression model was improved by 0.1397 and 0.088 compared with that of the traditional method. Compared with the pre-trained model based on point light source, it also achieved better results.