The　multiple　change　detection　(MCD)　of　hyperspectral　images　(HSIs)　is　the　process　of　detecting　change　areas　and　providing　＂from-to＂　change　information　of　HSIs　obtained　from　the　same　area　at　different　times.　HSIs　have　hundreds　of　spectral　bands　and　contain　a　large　amount　of　spectral　information.　However,　current　deep-learning-based　MCD　methods　do　not　pay　special　attention　to　the　interspectral　dependency　and　the　effective　spectral　bands　of　various　land　covers,　which　limits　the　improvement　of　HSIs＇　change　detection　(CD)　performance.　To　address　the　above　problems,　we　propose　a　spectrum-induced　transformer-based　feature　learning　(STFL)　method　for　HSIs.　The　STFL　method　includes　a　spectrum-induced　transformer-based　feature　extraction　module　(STFEM)　and　an　attention-based　detection　module　(ADM).　First,　the　3D-2D　convolutional　neural　networks　(CNNs)　are　used　to　extract　deep　features,　and　the　transformer　encoder　(TE)　is　used　to　calculate　self-attention　matrices　along　the　spectral　dimension　in　STFEM.　Then,　the　extracted　deep　features　and　the　learned　self-attention　matrices　are　dot-multiplied　to　generate　more　discriminative　features　that　take　the　long-range　dependency　of　the　spectrum　into　account.　Finally,　ADM　mines　the　effective　spectral　bands　of　the　difference　features　learned　from　STFEM　by　the　attention　block　(AB)　to　explore　the　discrepancy　of　difference　features　and　uses　the　softmax　function　to　identify　multiple　changes.　The　proposed　STFL　method　is　validated　on　two　hyperspectral　datasets,　and　their　experiments　illustrate　the　superiority　of　the　proposed　STFL　method　over　the　currently　existing　MCD　methods.