Bayesian network is a kind of uncertainty knowledge expression and reasoning tool, and it is an effective means to solve problems in related fields such as information retrieval. Considering the characteristics of e-commerce supply chain supply information and Bayesian network, a cognitive big data analysis method for intelligent information system is designed. The model uses a set of information sample documents to describe the query requirements and the documents to be detected. By calculating the similarity between them, the return results of the general search engine are sorted, thereby retrieving the supply chain supply information required by the user. Through numerical results, the precision of the source information retrieval model based on Bayesian network is also significantly higher than that of the trust network model and the inference network model, and the experimental data shows that the Bayesian network model has better retrieval performance than the trust network model and the inference network model. Therefore, when conducting large-scale e-commerce supply chain supply information collection, Bayesian network-based source information retrieval model is effective.

中文翻译：

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基于贝叶斯网络的电子商务供应链信息系统快速检索模型的构建

贝叶斯网络是一种不确定性知识表达和推理工具，是解决信息检索等相关领域问题的有效手段。针对电子商务供应链供应信息和贝叶斯网络的特点，设计了一种智能信息系统的认知大数据分析方法。该模型使用一组信息样本文档来描述查询要求和要检测的文档。通过计算它们之间的相似度，可以对通用搜索引擎的返回结果进行排序，从而检索用户所需的供应链供应信息。通过数值结果 基于贝叶斯网络的源信息检索模型的精度也大大高于信任网络模型和推理网络模型的实验数据，实验数据表明贝叶斯网络模型的检索性能优于信任网络模型和推理网络模型。推理网络模型。因此，在进行大规模电子商务供应链供应信息收集时，基于贝叶斯网络的源信息检索模型是有效的。