The　classical　belief　rule-based　(BRB)　systems　are　usually　constructed　by　arranging　and　combining　referential　values　of　antecedent　attributes　or　by　setting　special　fixed　values,　which　can　lead　to　overly　large　size　of　BRB　systems　in　complex　problems.　This　paper　combines　the　decision　tree　classification　method　to　analyze　the　information　of　data　and　extract　the　rules.　Based　on　this,　a　new　rule　representation　method　with　referential　interval　is　proposed　and　the　rule　base　is　constructed　according　to　the　support　degree　and　belief　degree　of　the　data.　In　the　newly　proposed　method,　the　introduction　of　decision　tree　ensures　that　the　size　of　the　rule　base　is　reasonable.　Moreover,　the　rule　parameters　trained　by　the　differential　evolution　(DE)　algorithm　are　optimized　and　adjusted　to　further　improve　the　system　performance.　The　experiments　are　conducted　on　several　commonly　used　public　classification　datasets.　And　the　proposed　algorithm　can　achieve　better　accuracy　results　compared　with　classical　classification　methods　and　the　existing　classification　methods　of　BRB　systems　on　average.　The　experimental　results　validate　the　reasonableness　and　effectiveness　of　the　BRB　construction　method　proposed　in　this　paper.