On　the　basis　of　the　standard　SVM　for　regression,　the　dynamic　Ε-SVM　method　was　proposed　to　establish　precise　mathematical　models　to　describe　the　behavior　of　biochemistry　systems,　namely　each　training　sample　used　different　error.　At　the　same　time,　an　improved　multi-objective　Genetic　Algorithm　(MOGA)　was　used　to　automatically　select　the　dynamic　Ε-SVM　parameters.　A　new　modeling　method　that　combined　improved　MOGA　with　dynamic　Ε-SVM　regression　was　presented.　The　model　for　titer　pre-estimate　was　developed　in　Matlab6.5　with　data　collected　from　real　plant.　The　model　possessed　the　strong　capability　of　fitting　and　generalization.　It　is　shown　that　the　method　achieves　significant　improvement　in　the　generalization　performance　in　comparison　with　the　modeling　method　based　on　MOGA　and　the　standard　SVM.