At　present,　the　theoretical　analysis　of　gas　station　oil　and　gas　data　is　weak,　and　there　is　no　unified　platform　for　collecting　and　uploading.　In　view　of　these　problems,　a　set　of　data　acquisition　and　mining　scheme　is　proposed.　The　Apriori　algorithm　is　used　to　correlate　the　current　environmental　data　of　oil　and　gas,　focusing　on　the　correlation　between　oil　and　gas　concentration　and　liquid　resistance　pressure,　tank　temperature,　tank　pressure,　time,　and　treatment　unit　emission　concentration.　In　addition,　we　designed　and　implemented　a　remote　online　monitoring　system　for　oil　and　gas　recovery　based　on　the　SSH　framework.　The　results　of　the　application　obtained　in　a　gas　station　in　Beijing　show　that　this　system　can　provide　the　reference　basis　for　the　intelligent　construction　for　the　gas　station　to　monitor　the　large　oil　and　gas　data.　The　results　of　data　mining　and　analysis　can　provide　accurate　and　objective　data　support　for　the　monitoring　personnel　of　gas　stations,　and　higher　priority　monitoring　for　the　heavy　point　data　segment.　It　has　reference　value　and　provides　a　good　technical　foundation　for　the　statistics　and　processing　of　oil　and　gas　data　in　the　follow-up　gas　stations.　©　Springer　Nature　Switzerland　AG　2019.