To　make　full　use　of　the　sequential　information　obtained　by　continuous　inverse　synthetic　aperture　radar　(ISAR)　imaging,　this　article　proposes　a　sequential　ISAR　target　classification　network　based　on　hybrid　transformer　(HT).　First,　a　temporal-spatial　encoder　based　on　the　attention　mechanism　is　designed　to　extract　long-term　and　global　features　from　sequential　images.　Meanwhile,　a　local　feature　encoder　based　on　the　3-D　convolution　neural　network　is　designed　to　extract　short-term　and　local　features.　Then,　the　above　two　features　are　fused　and　the　classification　labels　are　obtained　by　a　channel　encoder-decoder.　In　4-satellite　target　classification　experiments,　the　proposed　HT　shows　high　accuracy　and　robustness　to　the　unknown　image　scaling,　rotation,　and　combined　deformations.