The　austenite　grain　growth　plays　a　significant　role　in　improving　the　structures　and　properties　of　carbon　steels.　Modeling,　characterizing,　and　predicting　the　temporal　evolution　of　microstructures　is　crucial　for　understanding　the　relationship　of　processing-structure-property.　Therefore,　in　this　study,　we　designed　a　customized　in-situ　heating　device　that　could　be　installed　in　the　scanning　electron　microscope　chamber　to　observe　the　temporal　evolution　process　of　the　microstructures　of　45#　steel.　We　collected　the　in-situ　experimental　images　during　heating　for　downstream　deep　learning-based　grain　boundary　extraction,　spatiotemporal　sequence　characterization,　and　prediction.　The　proposed　pipeline　was　fully　verified　in　three　experimental　datasets　under　two　distinct　heating　schemes　in　terms　of　(1)　short-term　and　long-term　prediction,　(2)　feeding　different　lengths　of　input　sequence,　and　(3)　transferring　to　other　datasets.　The　proposed　pipeline　presented　a　high　performance　in　quantitative　evaluation　metrics　and　qualitative　comparisons　between　the　predicted　results　and　the　ground　truths　in　all　three　verification　scenarios.　These　results　are　inspiring　for　researchers　as　they　can　preview　the　evolutional　state　in　advance,　thereby　saving　money,　labor,　and　time　with　the　assistance　of　deep　learning-based　predictive　models.　We　believe　that　this　pipeline　opens　opportunities　for　modeling,　characterizing,　and　predicting　evolutional　state,　which　will　aid　in　the　analysis　of　processing-structure-property　relationship,　tailoring　heat　treatment,　and　improving　material　properties.　©　2024　Elsevier　Ltd