To address the problems associated with online detection, low recognition efficiency, and strong subjectivity of the flotation dosing state, this paper proposes a flotation dosing state recognition method based on multiscale convolutional neural network (CNN) features and ranks automatic encoder kernel extreme learning machine (RAE-KELM). First, the flotation foam image is subjected to non-subsampled Shearlet multiscale decomposition, and the CNN is used to extract the depth features of each scale image and perform multiscale feature fusion. Then, the RAE-KELM is constructed, and an improved bacterial foraging algorithm based on quantum computing is used to optimize the RAE-KELM parameters. Finally, the optimal RAE-KELM model is obtained through self-built dataset training to realize the adaptive recognition of the flotation dosing state. The experimental results demonstrate that the recognition accuracy of the method can reach 98.88%. Additionally, the method reduces manual interventions, which can improve production efficiency.