When　calculating　negative　individual　matching　degrees,　there　might　appear　negative　values　and　all　rules*　activation　weights　may　be　equal　to　zero.　To　address　this　problem,　this　paper　introduces　the　Euclidean　distance　which　is　based　on　attribute　weights　and　improves　the　traditional　similarity　computational　formula.　In　addition,　the　traditional　rule　activation　method　activates　all　rules　whose　activation　weights　are　greater　than　zero　without　considering　inconsistency　which　exists　in　the　activated　rules,　since　the　inconsistency　of　activated　rules　will　weaken　the　reasoning　performance　of　EBRB　systems.　Hence,　considering　the　inconsistency　existing　in　the　activated　rules,　a　new　rule　activation　method　of　EBRB　based　on　improved　similarity　measures　is　proposed.　Compared　with　traditional　rule　activation　method　in　the　EBRB,　the　proposed　approach　activates　rules　by　setting　thresholds.　And　these　activated　rules　are　not　only　greater　than　zero　but　also　have　the　smallest　inconsistency.　Finally,　the　pipeline　leak　detection　problemand　multiple　public　classification　datasets　have　been　employed　to　validate　the　efficiency　of　the　new　rule　activation　method.　The　experimental　results　show　that　the　proposed　method　based　on　improved　similarity　measures　can　improve　the　reasoning　accuracy　of　EBRB　systems.　©　2018,　Editorial　Department　of　Journal　of　University　of　Science　and　Technology　of　China.　All　rights　reserved.