In　this　work,　we　proposed　a　deeply-integrated　explainable　pre-trained　deep　learning　framework　with　stacked　denoising　autoencoders　in　the　assessment　of　slope　stability.　The　deep　learning　model　consists　of　a　deep　neural　network　as　a　trunk　net　for　prediction　and　autoencoders　as　branch　nets　for　denoising.　A　comprehensive　review　of　machine　learning　algorithms　in　slope　stability　evaluation　is　first　given　in　the　introduction　section.　A　series　of　530　data　is　then　collected　from　real　slope　records,　which　are　visualized　and　investigated　in　feature　engineering　and　further　preprocessed　for　model　training.　To　ensure　reliable　and　trustworthy　model　interpretability,　a　unified　model　from　both　local　and　global　perspectives　is　integrated　into　the　deep　learning　model,　which　incorporated　the　ad　hoc　back-propagation　based　Deep　SHAP,　perturbation　based　Kernel　SHAP　and　PDPs,　and　distillation　based　LIME　and　Anchors.　For　a　fair　evaluation,　repeated　stratified　10-fold　cross-validation　is　adopted　in　model　evaluation.　The　obtained　results　manifest　that　the　constructed　model　outperforms　commonly　used　machine　learning　methods　in　terms　of　accuracy　and　stability　on　the　real-world　slope　data.　The　explainable　model　provides　a　reasonable　explanation　and　validates　the　capability　of　the　proposed　model,　and　reflects　the　causes　and　dependencies　of　model　predictions　for　a　given　sample.