To address the limitations of visible image feature-driven flotation performance recognition method, a new flotation performance recognition method based on dual-modality multiscale images CNN features and adaptive deep autoencoder kernel extreme learning machine was proposed.First, the visible and infrared images of foam were decomposed by nonsubsampled shearlet multiscale transform, and a two-channel CNN network was developed to extract and fuse the features of the dual-modality multiscale images.Then, the CNN features were abstracted layer-by-layer in the deep learning network, which was connected by a series of two hidden layer autoencoder extreme learning machine.Then, the decision was made by mapping to a higher dimensional space through the kernel extreme learning machine.Finally, the quantum bacterial foraging algorithm was improved and applied to optimize the recognition model parameters. The experimental results show that the recognition accuracy using dual-modality multiscale CNN features is clearly better than that of single modality multiscale and dual-modality single scale CNN features at a confidence level of 2.65%. Further,the adaptive deep autoencoder kernel extreme learning machine has better classification accuracy and generalization performance. The average recognition accuracy of each working condition reaches 95.98%. The accuracy and stability of flotation performance recognition is considerably improved compared with the existing methods.