The　fuzzy　neural　network　has　a　strong　ability　in　solving　pattern　recognition,　function　approximation　and　control　problems,　so　it　plays　an　increasingly　important　role　in　the　field　of　artificial　intelligence.　Scholars　have　confirmed　that　the　initial　weights　have　a　great　impact　on　the　subsequent　learning　of　the　fuzzy　neural　network.　However,　due　to　the　complexity　and　uncertainty　of　the　black-box　model,　the　initialization　of　the　model　is　always　a　problem.　This　paper　proposes　a　weight　initialization　method　based　on　rule　partition　for　the　fuzzy　neural　network　to　solve　this　problem.　The　connection　weights　between　the　membership　function　layer　and　the　rule　layer　are　initialized　by　classifying　weight　intervals　according　to　different　rules,　which　could　reduce　the　similarity　of　fuzzy　rules.　Finally,　the　proposed　method　is　verified　by　some　experiments.　The　results　show　that　the　fuzzy　neural　network　initialized　by　rule　partition　achieves　better　generalization　performance,　which　proves　the　superiority　of　this　method.