Cyanobacterial harmful algal blooms (cyanoHABs) are extremely detrimental to the environment and cause sizable economic losses. Microcystis aeruginosa is reported to be inhibited by Eurasian watermilfoil (Myriophyllum spicatum), and onset of the inhibitory effects of M. spicatum varied depending on the seasons. This study aimed to investigate the seasonal allelopathy effects in the metabolomes of M. spicatum using gas chromatography–mass spectrometry and predict the most effective season for its allelopathic inhibitory effects on the growth of M. aeruginosa. A machine learning approach using multi-layer perceptron was used to predict the season with maximum anti-cyanobacterial potential. The prediction model suggested that M. spicatum collected in August would have higher growth-inhibiting effects than other months with 93.6 (± 2.9) likelihood. These results were consistent with coexistence experiments where M. spicatum collected in August showed the earliest onset of inhibition. The study concluded that the inhibitory potential of M. spicatum on cyanobacterial growth was strong in the summer, especially in August. This suggests that neural network machine learning can be applied to a variety of topics using accumulated data, making clearer and more useful predictions possible even in multivariate and complex environmental data.

蓝藻有害藻华（cyanoHAB）对环境极其有害，并造成巨大的经济损失。据报道，铜绿微囊藻受到欧亚狐尾藻 ( Myriophyllum spicatum ) 的抑制，而狐尾藻 (Myriophyllum spicatum)的抑制作用的起效因季节而异。本研究旨在利用气相色谱-质谱法研究穗状分枝杆菌代谢组的季节性化感作用，并预测其对铜绿分枝杆菌生长的化感抑制作用最有效的季节。使用多层感知器的机器学习方法用于预测抗蓝藻潜力最大的季节。预测模型表明， 8 月采集的穗花霉比其他月份具有更高的生长抑制作用，可能性为 93.6（± 2.9）。这些结果与共存实验一致，其中八月收集的穗分枝杆菌表现出最早的抑制作用。研究得出结论，穗花藻对蓝藻生长的抑制潜力在夏季特别是8月份较强。这表明神经网络机器学习可以利用积累的数据应用于各种主题，即使在多元和复杂的环境数据中也可以做出更清晰、更有用的预测。