Analyzing　medical　records　takes　a　large　amount　of　time　and　resources　for　medical　workers.　Therefore,　the　advancement　of　report　generation　technology　is　critical　for　saving　doctors＇　time　while　producing　reports.　Some　diseases　frequently　exhibit　co-occurrence　interactions　in　the　context　of　medical　picture　categorization　and　report　creation,　which　necessitate　hu-man-designed　and　added　representation.　To　further　capture　this　latent　information,　we　propose　creating　a　disease　co-occurrence　matrix　from　the　existing　dataset　and　training　a　graph　neural　network　on　it.　We　use　multi-label　contrastive　learning　to　assist　the　model　in　distinguishing　the　mutual　links　between　different　diseases.　Meanwhile,　we　incorporate　report　retrieval　module　and　decoder　model　to　complete　the　report　generation　task,　and　the　multi-task　learning　of　disease　classification　and　report　generation　can　improve　the　generalization　ability　of　the　target　task.　Experimental　results　show　that　our　pro-posed　combined　method　and　multi-task　learning　approach　have　shown　significant　improvement　compared　to　previous　research.