Since　the　extended　belief　rule　base　needs　to　iterate　by　all　the　unordered　rules　in　the　inference　process,　it　will　result　in　a　low　efficiency　of　the　belief　rule　base　in　system　inference　with　a　large　number　of　rules.　Therefore,　this　paper　proposes　to　use　the　Locality　Sensitive　Hashing　algorithm　to　index　the　confidence　rule.　First,　Locality　Sensitive　Hashing　is　used　to　generate　special　locality　sensitive　hash　value　for　all　the　rules　in　the　Extended　belief　rule　base　and　the　hash　value　can　keep　the　similarity　between　the　original　rules,　so　that　similar　rules　have　a　greater　probability　of　obtaining　the　same　index　value.　Then,　by　processing　the　input　data,　we　find　the　rules　that　are　adjacent　to　the　input　data　in　the　index　table,　and　selectively　activate　these　rules,　thus　improving　the　system＇s　inference　efficiency.　Finally,　by　choosing　a　nonlinear　function　fitting　experiment　and　a　simulation　experiment　on　oil　pipeline　leak　to　the　detection　Extended　belief　rule　base　system　based　on　the　Locality　Sensitive　Hashing　index,　experimental　results　show　that　the　Locality　Sensitive　Hashing　algorithms　can　effectively　optimize　the　Extended　belief　rule　base　system　inference　efficiency　and　improve　the　accuracy　of　the　output　results.　©　2019,　The　Editorial　Board　of　Journal　of　Xidian　University.　All　right　reserved.