Current　semantic　change　detection　(SCD)　methods　face　challenges　in　modeling　temporal　correlations　(TCs)　between　bitemporal　semantic　features　and　difference　features.　These　methods　lead　to　inaccurate　detection　results,　particularly　for　complex　SCD　scenarios.　This　paper　presents　a　hierarchical　semantic　graph　interaction　network　(HGINet)　for　SCD　from　high-resolution　remote　sensing　images.　This　multitask　neural　network　combines　semantic　segmentation　and　change　detection　tasks.　For　semantic　segmentation,　we　construct　a　multilevel　perceptual　aggregation　network　with　a　pyramidal　architecture.　It　extracts　semantic　features　that　discriminate　between　different　categories　at　multiple　levels.　We　model　the　correlations　between　bitemporal　semantic　features　using　a　TC　module　that　enhances　the　identification　of　unchanged　areas.　For　change　detection,　we　design　a　semantic　difference　interaction　module　based　on　a　graph　convolutional　network.　It　measures　the　interactions　among　bitemporal　semantic　features,　their　corresponding　difference　features,　and　the　combination　of　both.　Extensive　experiments　on　four　datasets,　namely　SECOND,　HRSCD,　Fuzhou,　and　Xiamen,　show　that　HGINet　performs　better　in　identifying　changed　areas　and　categories　across　various　scenarios　and　regions　than　nine　existing　methods.　Compared　with　the　existing　methods　applied　on　the　four　datasets,　it　achieves　the　highest　F1scd　values　of　59.48%,　64.12%,　64.45%,　and　84.93%,　and　SeK　values　of　19.34%,　14.55%,　18.28%,　and　51.12%,　respectively.　Moreover,　HGINet　mitigates　the　influence　of　fake　changes　caused　by　seasonal　effects,　producing　results　with　well-delineated　boundaries　and　shapes.　Furthermore,　HGINet　trained　on　the　Fuzhou　dataset　is　successfully　transferred　to　the　Xiamen　dataset,　demonstrating　its　effectiveness　and　robustness　in　identifying　changed　areas　and　categories　from　high-resolution　remote　sensing　images.　The　code　of　our　paper　is　accessible　at　https://github.com/long123524/HGINet-torch.