Remaining　fatigue　life　prediction　is　vital　for　engineering　structures　to　ensure　safety　and　reliability.　The　successive　and　dynamic　changes　in　morphology　are　valuable　informational　reminders　of　the　remaining　life　of　a　material.　This　study　proposed　a　novel　strategy　to　monitor　and　predict　the　remaining　life　of　materials　in　a　successive　and　dynamic　manner,　incorporating　the　in　-situ　scanning　electron　microscope　(SEM)　and　deep　learning　algorithm.　Two　in　-situ　SEM　fatigue　experiments　were　conducted　to　simulate　the　practical　service　conditions　of　additively　manufactured　Inconel　718　alloy.　A　series　of　SEM　image　sequences　reflecting　the　changing　morphology　were　collected　and　used　for　downstream　deep　learning　-based　remaining　life　prediction.　Original　SEM　images　were　cropped　and　grouped　for　training,　validation,　and　test　purposes.　Results　showed　that　the　deep　learning　-based　method　exhibited　high　prediction　ability.　The　proposed　method　＇s　predicted　life　values　were　highly　close　to　the　experimental　results.　The　visualization　result　showed　that　the　microstructure　evolution,　such　as　accumulation　of　slip　bands,　surface　fluctuation　changes　resulting　from　coordinated　grain　deformation,　and　fatigue　crack　growth,　provide　much　information　and　decision　support　to　the　network.　These　results　demonstrated　the　great　promise　of　monitoring　and　predicting　the　remaining　fatigue　life　of　materials　based　on　morphology.　We　believe　this　image　and　deep　learning　-based　strategy　will　inspire　current　fatigue　life　prediction　and　facilitate　the　move　toward　successive,　automatic,　and　accurate　life　monitoring　in　practical　engineering　structures.