To solve the problem that froth image is difficult to be accurately segmented due to its high complex-ity， this paper proposes a new I-Attention U-Net network for froth image segmentation. The algorithm uses the U-Net network as the backbone network and uses the Inception module to replace the first convolutional pooling layer so as to extract the multi-scale and multi-level shallow feature information of the froth image. A Pyramid pooling module is introduced to improve the segmentation effect by summing the feature maps of different scales. And the self-attention gating unit is improved to make it more suitable for the segmentation of flotation froth images， which strengthens the importance of deep features and performs reinforcement learning on froth edges of different sizes. The research results show that the Jaccard coefficient of the algorithm proposed in this paper is 91.73% and the Dice coefficient is 95.66%. Compared with the results of other similar segmentation algorithms， the Jaccard coefficient and Dice coefficient are increased by 1.59% and 0.88%， respectively. The model can better segment the zinc flotation froth image， and solve the problems of under-segmentation and over-segmentation， which is a good way for the follow-up. In addition， the method has less detection time and fewer model parameters and also has the ability to be deployed in industrial field computers， which has certain practical application value.