There are many different types of species distribution models (SDMs) that are widely used in the field of ecology. In this research, we explored a new advanced mechanism for predicting the distribution of species based on fuzzy membership function, principle of maximum entropy, fuzzy mathematics comprehensive evaluation, and the framework of Bayesian networks. We use fuzzy mathematics and Bayesian network model (FBM) to simulate relationships between species’ habitats and environmental variables, and the relationship may be difficult to quantify effectively. FBM, which combines species data, environmental data, expert experience, and machine learning, could reduce the data and system error. In the case of medicinal plant, Angelica sinensis (Oliv.) Diels, many approaches have been applied, including nine learning sequence of sampling sites, three FBM models, two types of information classification by fuzzy mathematical classification (FMC) and equal interval classification (EIC), and the evaluation of AIC and log-likelihood. Through the comparison of reasoning results between FBM and fuzzy matter element model (FME) in testing sites, the result shows that the combination of objective data and empirical model structure makes FBM have better result output. Besides, FBM sensitivity analysis helps researchers explore in detail the impact of environmental factors on each level of species habitat suitability. The temperature factor has an important influence on the highly suitable, moderately suitable, and lowly suitable habitats of A. sinensis. Through FMC and sensitivity analysis, annual mean temperature (Bio1) in 5.92 °C-9.05 °C and mean temperature of warmest quarter (Bio10) in 14.80 °C-18.60 °C are the highly suitable habitat temperature range of A. sinensis.

有许多不同类型的物种分布模型（SDM），广泛应用于生态学领域。在这项研究中，我们探索了一种基于模糊隶属函数，最大熵原理，模糊数学综合评估以及贝叶斯网络框架来预测物种分布的先进机制。我们使用模糊数学和贝叶斯网络模型（FBM）来模拟物种栖息地与环境变量之间的关系，这种关系可能难以有效地量化。结合物种数据，环境数据，专家经验和机器学习的FBM可以减少数据和系统错误。以药用植物当归为例（Oliv。）Diels，已经应用了许多方法，包括九个采样点学习序列，三个FBM模型，通过模糊数学分类（FMC）和等距分类（EIC）进行的两种信息分类，以及对AIC和AIC的评估。对数似然。通过对测试现场的FBM与模糊物元模型（FME）的推理结果进行比较，结果表明，客观数据与经验模型结构的结合使得FBM具有较好的结果输出。此外，FBM敏感性分析可帮助研究人员详细探索环境因素对物种栖息地适应性各个层面的影响。温度因子对中华按蚊的高适宜，中适宜和低适宜生境具有重要影响。。通过FMC和敏感性分析，年平均温度（Bio1）在5.92°C-9.05°C之间，而最温暖的四分之一温度（Bio10）在14.80°C-18.60°C之间是中华曲霉的高度适宜生境温度范围。