In　various　domains　spanning　materials　synthesis,　chemical　catalysis,　life　sciences,　and　energy　materials,　in　situ　transmission　electron　microscopy　(TEM)　methods　exert　a　profound　influence.　These　methodologies　enable　the　real-time　observation　and　manipulation　of　gas-phase　and　liquid-phase　reactions　at　the　nanoscale,　facilitating　the　exploration　of　pivotal　reaction　mechanisms.　Fundamental　research　areas　like　crystal　nucleation,　growth,　etching,　and　self-assembly　have　greatly　benefited　from　these　techniques.　Additionally,　their　applications　extend　across　diverse　fields　such　as　catalysis,　batteries,　bioimaging,　and　drug　delivery　kinetics.　However,　the　intricate　nature　of　‘soft　matter’　presents　a　challenge　due　to　the　unique　molecular　properties　and　dynamic　behavior　of　these　substances　that　remain　insufficiently　understood.　Investigating　soft　matter　within　in　situ　liquid-phase　TEM　settings　demands　further　exploration　and　advancement　compared　to　other　research　domains.　This　research　harnesses　the　potential　of　in　situ　liquid-phase　TEM　technology　while　integrating　deep　learning　methodologies　to　comprehensively　analyze　the　quantitative　aspects　of　soft　matter　dynamics.　This　study　centers　on　diverse　phenomena,　encompassing　surfactant　molecule　nucleation,　block　copolymer　behavior,　confinement-driven　self-assembly,　and　drying　processes.　Furthermore,　deep　learning　techniques　are　employed　to　precisely　analyze　Ostwald　ripening　and　digestive　ripening　dynamics.　The　outcomes　of　this　study　not　only　deepen　the　understanding　of　soft　matter　at　its　fundamental　level　but　also　serve　as　a　pivotal　foundation　for　developing　innovative　functional　materials　and　cutting-edge　devices.　©　2024　The　Royal　Society　of　Chemistry.