为提高重组Bacillus subtilis发酵生产L-天冬酰胺酶(EC 3.5.1.1，L-Asparaginase，L-ASNase)的水平，应用人工神经网络算法对其发酵培养基进行优化。首先通过单因素实验和Plackett-Burman实验筛选显著因素，再进行中心组合实验建立实验数据样本，最后利用JMP10.0建立神经网络模型优化发酵培养基组成。经优化，获得最佳培养基组成：蔗糖65 g/L、酵母蛋白胨28 g/L、玉米浆11 g/L、KH2PO4 11.5 g/L、NaCl 3.3 g/L、(NH4)2SO4 4 g/L、K2HPO4·3H2O 22.5 g/L、MgSO4·7H2O 1 g/L、L-天冬酰胺2 g/L。在该培养基条件下，L-ASNase产量达到515.6 U/mL，较优化前提高了90.9%。研究结果为L-ASNase上罐优化提供了基础数据。

In order to improve the yield of L-asparaginase (EC 3.5.1.1, L-ASNase) produced by recombinant Bacillus subtilis, the fermentation medium was optimized by using artificial neural network algorithm. First, the significant factors were screened through single factor experiment and Plackett-Burman experiment. Then the central composite experiment was used to establish the experimental data samples. Finally, a neural network model was established by using JMP10.0 to optimize the composition of fermentation medium. After optimization, the best medium was obtained as follows: sucrose 65 g/L, yeast peptone 28 g/L, corn syrup 11 g/L, KH2PO4 11.5 g/L, NaCl 3.3 g/L, ammonium sulfate 4 g/L, K2HPO4·3H2O 22.5 g/L, MgSO4·7H2O 1 g/L, and L-asparagine 2 g/L. L-ASNase activity reached 515.6 U/mL in the optimal culture medium, which was 90.9% higher than that before optimization. These results presented a basic data for jar fermentation optimization of L-ASNase.