The invention discloses a no-reference image quality evaluation method based on deep forest classification. The method comprises the following steps: step 1, image classification; step 2, extracting color quality characteristics of the image; step 3, extracting texture quality characteristics of the image; step 4, simulating the difference of different people on image quality cognition by utilizing the difference of decision tree extraction features in the deep forest classification model, and constructing the deep forest classification model to classify the image quality, including a multi-granularity scanning forest and a cascade forest; step 5, training the deep forest classification model based on the image quality features and the category labels thereof to obtain the probability thatthe test image belongs to different categories, i.e., statistical information of subjective evaluation results of different people on the image quality; step 6, setting a quality anchor, and fully considering the difference in the subjective evaluation process in combination with the probability that the image belongs to different categories to obtain a final image quality score. According to thenon-reference image quality evaluation method, the difference of different people for image quality cognition is simulated by using the deep forest, so that an image quality evaluation result is given. The method has important theoretical significance and practical value.