Optimization of Subcritical Water Extraction for Artemisia argyi Leaf Polysaccharides Using a Hybrid RSM–NN–DSA Framework

Subcritical water extraction (SWE) is an eco-friendly and efficient technique for isolating bioactive ingredients from natural products. To improve the extraction yield of Artemisia argyi leaf polysaccharides (AAPs), a three-stage hybrid optimization strategy combining single-factor experiments, response surface methodology (RSM), neural network (NN), and direct search algorithm (DSA) was proposed. Single-factor experiments were used to screen key parameters. A Box–Behnken design (BBD)-based RSM was applied for preliminary optimization. A {3, 5, 1} structured NN was trained using 63 datasets from RSM, and DSA was used to determine the globally optimal process parameters. The optimal conditions were obtained as follows: extraction time 17.72 min, liquid-to-solid ratio 92.83 mL/g, extraction temperature 123.35 °C, stirring speed 1800 r/min, and natural pH. Under these conditions, the experimental AAP extraction yield reached 6.99%, with a relative error of only 1.16% compared with the predicted value of 6.91%. Fourier transform infrared (FT-IR) spectroscopy confirmed that the product exhibited typical polysaccharide structural characteristics. The integrated RSM–NN–DSA framework provides a reliable and high-precision approach for optimizing SWE of plant polysaccharides, showing good potential for industrial applications.