Rapid　identification　of　peanut　seed　quality　is　crucial　for　public　health.　In　this　study,　we　present　a　terahertz　wave　imaging　system　using　a　convolutional　neural　network　(CNN)　machine　learning　approach.　Terahertz　waves　are　capable　of　penetrating　the　seed　shell　to　identify　the　quality　of　peanuts　without　causing　any　damage　to　the　seeds.　The　specificity　of　seed　quality　on　terahertz　wave　images　is　investigated,　and　the　image　characteristics　of　five　different　qualities　are　summarized.　Terahertz　wave　images　are　digitized　and　used　for　training　and　testing　of　convolutional　neural　networks,　resulting　in　a　high　model　accuracy　of　98.7%　in　quality　identification.　The　trained　THz-CNNs　system　can　accurately　identify　standard,　mildewed,　defective,　dried　and　germinated　seeds,　with　an　average　detection　time　of　2.2　s.　This　process　does　not　require　any　sample　preparation　steps　such　as　concentration　or　culture.　Our　method　swiftly　and　accurately　assesses　shelled　seed　quality　non-destructively.