Flavonoids are the secondary products of liquid fermentation of Phellinus igniarius and have important medical value. In this study, a hybrid intelligent algorithm combining Particle Swarm Optimization (PSO) and BP neural network is proposed to optimize the experimental environment of fermentation of Phellinus igniarius and to improve the flavonoids yield. The BP neural network is trained based on the 25 groups of experimental data and as the prediction model of flavonoid production. The experiment is compared with the mathematical regression model in the traditional response surface methodology to predict the accuracy increased by 15%. The BP neural network prediction model was used as an evaluation function in combination with PSO algorithm to optimize the experimental environment. According to the data simulation experiment, the best culture conditions of the liquid fermentation of Phellinus igniarius were obtained. The yield of Phellinus flavonoids from the previous about 1532.83 μg/mL to about 1896.4 μg/mL, yield increased by about 23.72%.