This study systematically optimized the fermentation process for fructosyltransferase (FTase) production by Aspergillus niger FS054, integrating traditional experimental designs with machine learning approaches. Single–factor experiments initially identified critical medium components (carbon source, nitrogen sources, phosphate, and metal ions) and cultivation parameters (pH, liquid volume, inoculum size, temperature, and shaking speed). Subsequent Plackett–Burman screening identified sucrose, yeast extract paste, and \(\hbox {NH}_4\hbox {Cl}\) as the most influential medium factors. Through Box–Behnken response surface methodology (RSM), the optimal medium composition was determined as sucrose 156.65 g/L, yeast extract paste 42 g/L, and \(\hbox {NH}_4\hbox {Cl}\) 1.68 g/L, yielding an enzyme activity of 3249.00 ± 24.39 U/L (99.16% agreement with RSM predictions). Further optimization of cultivation conditions using a hybrid backpropagation neural network–genetic algorithm (BP–GA) model identified optimal parameters as pH 5.5, a liquid volume of 96.6 mL (in a 250 mL shaker), and inoculum size of 2.4 \(\times\) \(10^{4}\) spores/mL, achieving a final enzyme activity of 3422.14 ± 36.86 U/L (1.1% deviation from the predicted 3460 U/L), representing a 4.2-fold increase over initial conditions. This work demonstrates the synergistic application of classical experimental design and artificial intelligence, significantly enhancing FTase productivity and potentially offering a more economical enzyme source for industrial–scale fructooligosaccharide (FOS) biosynthesis.
