In order to solve such problems as irregular particle shape, fuzzy edges, high adhesion and difficult to identify in the process of sinter particle size recognition, an improved UNet network based on multi-scale feature fusion to segment sinter images is proposed. By introducing residuals and dense connections and adding the SE module for feature fusion, a low-level accurate profile information is extracted and the problem of low utilization rate of sinter profile features is solved. Multi-scale dense connection is used by each layer to complete asymmetric information exchange, which effectively alleviates the semantic gap between encoded features and decoded features. Two decoders are added to the decoder part, the sinter profile feature is introduced, the multi-task learning is carried out, a new loss function is formed, and the profile information is used to constrain the mask segmentation of the sinter object, so as to improve the ability of the adhesion sinter profile segmentation. The results show that the improved UNet network significantly improves the segmentation accuracy of the adhesion sinter image compared with other networks, and lays a foundation for the accurate identification of sinter particle size.