A　new　algorithm　of　image　classification　based　on　the　sparse　autoencoder　and　the　support　vector　machine　was　proposed　in　view　of　the　drawbacks　that　the　single　layer　sparse　autoencoder　for　feature　learning　is　easy　to　lose　the　deep　abstract　feature　and　the　features　lack　the　robustness.　The　deep　sparse　autoencoder　is　constructed　to　learn　each　image　layer　and　the　feature　of　each　layer　is　automatically　extracted.　The　each　feature　weights　and　the　reorganized　set　of　feature　are　obtained　according　to　the　feature　weighting　method.　By　combining　the　strong　global　search　ability　of　genetic　algorithm　and　the　excellent　performance　of　support　vector　machine,　the　image　classification　is　completed　efficiently　and　accurately.　The　experimental　results　show　that　the　proposed　algorithm　can　automatically　learn　the　deep　feature　of　the　image,　and　the　reorganized　feature　has　high　feature　discrimination　ability,　which　effectively　improves　the　accuracy　of　image　classification.　©　2018,　The　Editorial　Board　of　Journal　of　System　Simulation.　All　right　reserved.