A　reliable　and　effective　diagnosis　of　coating　structure　is　important　for　further　maintenance.　For　the　problem　of　slow　detection　and　identification　using　terahertz　non-destructive　testing　technology　in　the　industrial　inspection,　a　rapid　diagnosis　algorithm　based　on　the　wavelet　packet　energy　and　support　vector　machine　method　was　developed　for　quick　evaluation　of　the　epoxy　protective　coating.　The　process　mainly　included　time　domain　signal　acquisition　of　various　epoxy　protective　coating　samples　detected　by　a　terahertz　pulse　imaging　system,　wavelet　packet　energy　parameters　extraction　as　the　diagnosis　feature　vectors,　classification　model　establishment　based　on　the　support　vector　machine　algorithm　and　coating　status　evaluation　using　a　three-class　classifier.　The　influence　on　classification　accuracy　by　the　various　feature　vectors　inputs　with　the　support　vector　machine　classifier　was　analysed.　Satisfying　results　were　achieved　when　the　relative　wavelet　packet　energy　was　taken　as　diagnostic　features.　A　strong　defective　area　could　be　quickly　identified　and　more　detail　targeted　analysis　could　be　implemented　as　needed.　The　time　spent　was　significantly　reduced　compared　to　the　terahertz　imaging　of　the　whole　area　along　with　the　manual　judgement.　The　analysis　indicated　that　the　proposed　method　would　be　very　useful　and　can　be　effectively　employed　for　the　coating　monitoring　application.