Despite the advancements in neural network technologies driving interactive image segmentation forward, challenges persist, especially concerning segmentation ambiguities caused by overlapping or visually similar objects against complex backgrounds, as well as intricate object boundaries. Addressing these challenges, we introduce FusionNet, focusing on effective feature fusion. Firstly, the Hierarchical Context Fusion Module aids in grasping holistic structures and multi-scale contextual information of target objects. Secondly, the Attention Feature Fusion Module captures more representative feature expressions. This design empowers FusionNet to capture details and contextual relationships better, thereby enhancing segmentation accuracy. For fine-grained boundary details, we propose the Local Correction Module, refining local mask details meticulously. This module initially focuses on information around newly clicked areas, employing discriminative correction feedback for enhanced detail processing accuracy. Rigorous experimentations on datasets like SBD, DAVIS, GrabCut, and Berkeley validate our model’s effectiveness, with segmentation results strongly supporting the superiority of our approach.