In　this　paper,　we　propose　HSENet,　a　hierarchical　semantic-enriched　network　capable　of　generating　high-quality　fused　images　with　robust　global　semantic　consistency　and　excellent　local　detail　representation.　The　core　innovation　of　HSENet　lies　in　its　hierarchical　enrichment　of　semantic　information　through　semantic　gathering,　distribution,　and　injection.　Specifically,　the　network　begins　by　balancing　global　information　exchange　via　multi-scale　feature　aggregation　and　redistribution　while　dynamically　bridging　fusion　and　segmentation　tasks.　Following　this,　a　progressive　semantic　dense　injection　strategy　is　introduced,　employing　dense　connections　to　first　inject　global　semantics　into　highly　consistent　infrared　features　and　then　propagate　the　semantic-infrared　hybrid　features　to　visible　features.　This　approach　effectively　enhances　semantic　representation　while　minimizing　high-frequency　information　loss.　Furthermore,　HSENet　includes　two　types　of　feature　fusion　modules,　to　leverage　cross-modal　attention　for　more　comprehensive　feature　fusion　and　utilize　semantic　features　as　a　third　input　to　further　enhance　the　semantic　representation　for　image　fusion.　These　modules　achieve　robust　and　flexible　feature　fusion　in　complex　scenarios　by　dynamically　balancing　global　semantic　consistency　and　fine-grained　local　detail　representation.　Our　approach　excels　in　visual　perception　tasks　while　fully　preserving　the　texture　features　from　the　source　modalities.　The　comparison　experiments　of　image　fusion　and　semantic　segmentation　demonstrate　the　superiority　of　HSENet　in　visual　quality　and　semantic　preservation.　The　code　is　available　at　https://github.com/Lxyklmyt/HSENet.