Cumulative　belief　rule-based　system　(CBRBS)　is　a　recent　representative　of　explainable　artificial　intelligence　(XAI).　However,　the　use　of　CBRBS　as　XAI　still　faces　many　challenges,　e.g.,　over-reliance　on　expert　experience　and　applying　unreasonable　rule　synthesis　in　the　existing　modeling　process.　Hence,　a　novel　modeling　approach　is　proposed　for　constructing　CBRBS　in　the　aim　of　providing　a　better　XAI,　in　which　a　joint　optimization　model　is　proposed　first　to　describe　the　mathematical　model　of　parameter　and　structure　optimization,　and　the　corresponding　algorithm　is　further　designed　to　automatically　achieve　the　joint　optimization　of　CBRBS.　Afterward,　a　domain-based　calculation　method　of　synthesis　factor　is　proposed　to　develop　a　new　rule　synthesis　method　for　CBRBS,　which　not　only　achieves　the　reduction　of　inefficient　and　inconsistent　rules　but　also　takes　into　account　interpretability　and　generalization　ability.　In　experimental　analysis,　the　proposed　modeling　approach　is　employed　to　construct　CBRBS　for　handling　rice　taste　assessment　and　benchmark　classification　problems.　The　comparison　results　show　that　the　proposed　approach　makes　it　possible　for　CBRBS　to　achieve　a　good　balance　between　model　complexity　and　inference　accuracy.　More　importantly,　the　resulting　CBRBS　has　better　accuracy　and　lower　complexity　than　some　existing　rule-based　systems　and　classical　classifiers.