Ganglong Fan

As an important infrastructure in the era of big data, the knowledge graph can integrate and manage data resources. Therefore, the construction of tourism knowledge graphs with wide coverage and of high quality in terms of information from the perspective of tourists’ needs is an effective solution to the problem of information clutter in the tourism field. This paper first analyzes the current state of domestic and international research on constructing tourism knowledge graphs and highlights the problems associated with constructing knowledge graphs, which are that they are time-consuming, laborious and have a single function. In order to make up for these shortcomings, this paper proposes a set of systematic methods to build a tourism knowledge graph. This method integrates the BiLSTM and BERT models and combines these with the attention mechanism. The steps of this methods are as follows: First, data preprocessing is carried out by word segmentation and removing stop words; second, after extracting the features and vectorization of the words, the cosine similarity method is used to classify the tourism text, with the text classification based on naive Bayes being compared through experiments; third, the popular tourism words are obtained through the popularity analysis model. This paper proposes two models to obtain popular words: One is a multi-dimensional tourism product popularity analysis model based on principal component analysis; the other is a popularity analysis model based on emotion analysis; fourth, this paper uses the BiLSTM-CRF model to identify entities and the cosine similarity method to predict the relationship between entities so as to extract high-quality tourism knowledge triplets. In order to improve the effect of entity recognition, this paper proposes entity recognition based on the BiLSTM-LPT and BiLSTM-Hanlp models. The experimental results show that the model can effectively improve the efficiency of entity recognition; finally, a high-quality tourism knowledge was imported into the Neo4j graphic database to build a tourism knowledge graph.

Through the analysis of the current research situation at home and abroad, this article finds that there is a lack of evaluation standards and methods in the virtual simulation experiment of e-commerce logistics smart warehousing. Therefore, it seriously affects the standardization and rationality of the experiment. To solve the problems in the evaluation of the current virtual simulation experiment, this article proposes a virtual simulation experiment evaluation model of e-commerce logistics smart warehousing based on multidimensional weighting. This article firstly sorts out the basic process of e-commerce logistics smart warehousing experiment activities and establishes the evaluation object. Then, based on the duality degree of the output results of the experimental steps, it proposes a method that conforms to the corresponding operation steps. Thus, a three-dimensional evaluation model of the completion degree of the operation steps, the reasonable degree of the operation steps, and the completion time of the operation steps are constructed. An automatic scoring evaluation model is proposed based on the combination of three-dimensional weighted evaluation of experimental steps. Finally, the feasibility and convenience of the evaluation model are verified through the experiment analysis.