Radial　basis　function　neural　network　(RBFNN)　has　been　widely　used　in　industrial　process　modeling　because　of　its　strong　approximation　ability.　However,　many　existing　modeling　methods　aim　at　accuracy,　but　ignore　the　stability　of　mode.　Therefore,　this　paper　proposes　a　parameter　optimization　method　of　RBF　neural　network　based　on　modified　Levenberg-Marquardt　(MLM-RBFNN)　to　ensure　the　stability　of　the　network.　Firstly,　a　typical　sample　mechanism　with　variance　reduction　is　proposed,　which　can　reduce　the　error　of　gradient　estimation　and　use　accurate　gradient　information　to　guide　learning.　Secondly,　a　modified　LM　optimization　algorithm　is　proposed　to　optimize　the　parameters,　which　not　only　improve　the　convergence　speed　of　the　network,　but　also　ensure　the　stability　of　the　model.　Finally,　a　multi-step　updating　rule　based　on　a　typical　sample　and　a　single　sample　is　designed,　which　effectively　reduces　the　sample　bias　introduced　by　a　single　sample.　In　order　to　prove　the　advantages　of　the　MLM-RBFNN　method　proposed　in　this　paper,　experiments　are　carried　out　on　three　benchmark　data　sets　and　a　practical　wastewater　treatment　process　application　problem　and　compared　with　several　existing　methods.　The　results　show　that　the　proposed　MLM-RBFNN　method　has　good　performance　in　both　learning　speed　and　stability.