Recently,　deep　learning-based　methods　have　made　impressive　progress　in　infrared　small　target　detection　(IRSTD).　However,　the　weak　and　variable　nature　of　small　targets　constrains　the　feature　extraction　and　scene　adaptation　of　existing　methods,　leading　to　low　data　utilization　and　poor　robustness.　To　address　this　issue,　we　innovatively　introduce　the　feedback　mechanism　into　IRSTD　and　propose　the　dynamic　feedback　iterative　network　(DFINet).　The　main　motivation　is　to　guide　the　model　training　and　prediction　utilizing　the　history　prediction　mask　(HPMK)　of　previous　rounds.　On　the　one　hand,　in　the　training　phase,　DFINet　can　further　mine　the　key　features　of　real　targets　by　training　in　multiple　iterations　with　limited　data;　on　the　other　hand,　in　the　prediction　phase,　DFINet　can　correct　the　wrong　results　through　feedback　iterative　to　improve　the　model　robustness.　Specifically,　we　first　propose　the　dynamic　feedback　feature　fusion　module　(DFFFM),　which　dynamically　interacts　HPMK　with　feature　maps　through　a　hard　attention　mechanism　to　guide　feature　mining　and　error　correction.　Then,　for　better　feature　extraction,　the　cascaded　hybrid　pyramid　pooling　module　(CHPP)　is　devised　to　capture　both　global　and　local　information.　Finally,　we　propose　the　dynamic　semantic　fusion　module　(DSFM),　which　innovatively　utilizes　feedback　information　to　guide　the　fusion　of　high-level　and　low-level　features　for　better　feature　representation　in　different　scenarios.　Extensive　experimental　results　on　publicly　available　datasets　of　NUDT-SIRST,　IRSTD-1k,　and　SIRST　Aug　show　that　DFINet　outperforms　several　state-of-the-art　methods　and　achieves　superior　detection　performance.　Our　code　will　be　publicly　available　at　https://github.com/uisdu/DFINet.