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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.

Power system load forecasting model plays an important role in all aspects of power system planning, operation and control. Therefore, accurate power load forecasting provides an important guarantee for the stable operation of the power grid system. This paper first analyzes the current status of power system load forecasting, and finds that there are still some deficiencies in the existing forecasting models. In order to make up for these shortcomings, this paper proposes construction of short-term and mid-term power system load forecasting models based on hybrid deep learning. In the data preprocessing part, this paper proposes to use the exponential weight moving average method to process missing values. The detection method of abnormal value is the GeneralizedESDTestAD(GESD). This paper analyzes the historical load data of a regional power grid and four industries, and proposes a short-term power system load forecasting model based on Bi-directional Long Short-Term Memory(BiLSTM); For mid-term load forecasting, this paper first uses random forest and Pearson correlation coefficient to select features. Then a hybrid deep learning model is constructed based on BiLSTM and random forest. After optimizing the parameters of the model, a mid-term power system load forecasting model based on hybrid deep learning is constructed. Finally, the benchmark models are selected for comparative experiments. The experimental results show that the MAPE of the proposed model is 2.36%, which is better than the benchmark models. This proves that the proposed hybrid model can effectively improve the accuracy of power system load forecasting.

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