Ensuring　road　safety　is　heavily　reliant　on　the　effective　maintenance　of　vehicles.　Accurate　predictions　of　maintenance　requirements　can　substantially　reduce　ownership　costs　for　vehicle　owners.　Consequently,　this　field　has　attracted　increasing　attention　from　researchers　in　recent　years.　However,　existing　studies　primarily　focus　on　predicting　a　limited　number　of　maintenance　needs,　predominantly　based　solely　on　vehicle　mileage　and　driving　time.　This　approach　often　falls　short,　as　it　does　not　comprehensively　monitor　the　overall　health　condition　of　vehicles,　thus　posing　potential　safety　risks.　To　address　this　issue,　we　propose　a　deep　fusion　network　model　that　utilizes　multi-source　data,　including　vehicle　maintenance　record　data　and　vehicle　base　information　data,　to　provide　comprehensive　predictions　for　vehicle　maintenance　projects.　To　capture　the　relationships　among　various　maintenance　projects,　we　create　a　correlation　representation　using　the　maintenance　project　co-occurrence　matrix.　Furthermore,　building　on　the　correlation　representation,　we　propose　a　deep　fusion　network　that　employs　the　attention　mechanism　to　efficiently　merge　vehicle　mileage　and　vehicle　base　information.　Experiments　conducted　on　real　data　demonstrate　the　superior　performance　of　our　proposed　model　relative　to　competitive　baseline　models　in　predicting　vehicle　maintenance　projects.　©　2024　by　the　authors.