Electronic　toll　collection　(ETC)　data　mining　has　become　one　of　the　hotspots　in　the　research　of　intelligent　expressway　extension　applications.　Ensuring　the　integrity　of　ETC　data　stands　as　a　critical　measure　in　upholding　data　quality.　ETC　data　are　typical　structured　data,　and　although　deep　learning　holds　great　potential　in　the　ETC　data　restoration　field,　its　applications　in　structured　data　are　still　in　the　early　stages.　To　address　these　issues,　we　propose　an　expressway　ETC　missing　transaction　data　restoration　model　considering　multi-attribute　features　(MAF).　Initially,　we　employ　an　entity　embedding　neural　network　(EENN)　to　automatically　learn　the　representation　of　categorical　features　in　multi-dimensional　space,　subsequently　obtaining　embedding　vectors　from　networks　that　have　been　adequately　trained.　Then,　we　use　long　short-term　memory　(LSTM)　neural　networks　to　extract　the　changing　patterns　of　vehicle　speeds　across　several　continuous　sections.　Ultimately,　we　merge　the　processed　features　with　other　features　as　input,　using　a　three-layer　multilayer　perceptron　(MLP)　to　complete　the　ETC　data　restoration.　To　validate　the　effectiveness　of　the　proposed　method,　we　conducted　extensive　tests　using　real　ETC　datasets　and　compared　it　with　methods　commonly　used　for　structured　data　restoration.　The　experimental　results　demonstrate　that　the　proposed　method　significantly　outperforms　others　in　restoration　accuracy　on　two　different　datasets.　Specifically,　our　sample　data　size　reached　around　400,000　entries.　Compared　to　the　currently　best　method,　our　method　improved　the　restoration　accuracy　by　19.06%　on　non-holiday　ETC　datasets.　The　MAE　and　RMSE　values　reached　optimal　levels　of　12.394　and　23.815,　respectively.　The　fitting　degree　of　the　model　to　the　dataset　also　reached　its　peak　((Formula　presented.)　=　0.993).　Meanwhile,　the　restoration　stability　of　our　method　on　holiday　datasets　increased　by　5.82%.　An　ablation　experiment　showed　that　the　EENN　and　LSTM　modules　contributed　7.60%　and　9%　to　the　restoration　accuracy,　as　well　as　4.68%　and　7.29%　to　the　restoration　stability.　This　study　indicates　that　the　proposed　method　not　only　significantly　improves　the　quality　of　ETC　data　but　also　meets　the　timeliness　requirements　of　big　data　mining　analysis.　©　2023　by　the　authors.