Background:　Gastric　cancer　is　the　third　most　common　cause　of　cancer-related　death.　Accurate　preoperative　prediction　of　lymph　node　metastasis　(LNM)　in　primary　gastric　cancer　strongly　influences　the　choice　of　surgical　approach　and　the　prognosis　of　gastric　cancer　patients.Objective:　To　develop　and　validate　a　deep　learning-based　model　to　analyze　routine　histological　slides　of　patients　with　primary　gastric　cancer　as　well　as　clinical　data　to　predict　the　occurrence　of　LNM　preoperatively.Patients　and　Methods:　Radical　surgery　slides　from　309　patients　and　biopsy　slides　from　157　patients　were　collected　from　Fujian　Cancer　Hospital,　along　with　radical　surgery　slides　from　306　patients　from　The　Cancer　Genome　Atlas　(TCGA).　Clinical　data,　including　age,　gender,　lauren　classification,　and　tumor　location,　were　collected.　These　datasets　were　used　to　develop　and　validate　a　deep　learning-based　model.Results:　Five　models　were　trained　via　cross-validation,　with　a　mean　area　under　the　receiver　operating　characteristic　curve　(AUC)　(standard　deviation　[SD])　of　0.877　(0.048)　achieved.　There　was　a　significant　difference　in　scores　between　both　classes　(LNM　positive　[N+]　and　LNM　negative　[N0])　(　p＜　0.001).　we　validated　the　performance　of　the　model　on　biopsy　slides　and　achieved　a　mean　AUC　(SD)　of　0.725　(0.020).　In　the　analysis　of　clinical　data,　the　lauren　classification　was　demonstrated　to　be　an　independent　risk　factor　for　predicting　LNM.Conclusion:　Our　study　confirmed　that　deep　learning-based　image　analysis　could　preoperatively　predict　LNM　in　patients　with　primary　gastric　cancer,　combining　histological　slides　at　different　magnification　scales　and　relevant　clinical　data,　showing　superiority　over　individual　modality　prediction.