Nasotracheal　intubation　(NTI)　is　one　of　the　most　commonly　performed　procedures　in　anesthesia　and　is　considered　the　gold　standard　for　securing　the　airway　of　patients.　Endoscope　operation　is　critical　to　the　success　of　NTI.　However,　this　operation　remains　challenging　as　it　requires　the　surgeon　to　classify　anatomical　landmarks　and　detect　heading　targets　of　the　endoscope　tip　in　a　sequence　of　monocular　images.　To　alleviate　this　problem,　this　study　presents　a　learning-based　navigation　method　that　automatically　classifies　four　different　anatomical　landmarks　and　detects　the　heading　target　of　the　endoscope　tip　from　endoscopic　images.　First,　an　end-to-end　multitask　network　is　introduced　that　consists　of　a　branch　for　anatomical　landmark　classification　and　another　for　heading　target　detection.　In　addition,　a　convolutional　attention　module　is　designed　to　improve　network　performance　by　combining　spatial　and　channel　attention.　Second,　an　endoscopic　dataset　named　intuNav　is　built　for　network　training.　The　trained　network　calculates　navigation　information　without　image　prior　knowledge.　Finally,　extensive　experiments　on　the　built　dataset　and　endoscopic　videos　demonstrate　the　high　performance　of　our　method,　achieving　classification　(94%)　and　detection　(79.4%)　accuracies.　The　results　also　indicate　that　the　proposed　method　is　effective　and　efficient　in　navigation　generation　during　NTI.