Industrial　process　data　are　time　series　data　with　strong　dynamics　and　nonlinearities　and　are　based　on　temporal　slowness.　For　industrial　soft　sensor　modeling,　it　is　critical　to　extract　the　slowness　features　from　strongly　nonlinear　and　dynamic　process　variables.　To　address　these　issues,　a　method　based　on　a　Siamese　autoencoder　and　long　short-term　memory　(SiameseAE-LSTM)　network　is　proposed.　First,　a　SiameseAE　network　that　considers　both　nonlinearities　and　dynamics　is　proposed　for　essential　slow　feature　extraction.　Then,　a　slow　feature　matrix　is　extracted　and　augmented　with　historical　slow　features　and　quality　variables,　ensuring　the　validity　of　slow　features.　This　matrix　contains　more　process　information.　Finally,　an　LSTM　network　is　used　to　capture　the　long-term　dependencies　from　the　feature　matrix　to　build　a　soft　sensor　model.　The　effectiveness　and　superiority　of　the　proposed　method　are　demonstrated　on　a　numerical　example　and　two　industrial　process　cases.