This paper established a numerical simulation model to analyze the pressure transient and rate transient behaviors in reservoir with complex fracture network. Firstly, the fractures are introduced into the coordinate system through the position, angle, and length. Secondly, a mathematical model is established by using unstable seepage model. Thirdly, the central difference method was used to solve the model and local grid refinement method is introduced to describe the network fractures. Finally, we compared the results obtained from this paper’s model with the production data. The results show acceptable and reasonable matches for typical well. Meanwhile, the sensitivity of two properties is discussed. The model solution is verified with an analytical method thoroughly. The novelty of this paper is to introduce each fracture in fracture network into the coordinate system. Then, the grid refinement is achieved according to the fracture information. The presented new model simplifies the analysis of the pressure transient and rate transient of the reservoir with complex fracture network, and it is more efficient than the conventional numerical method. Compared with the analytical methods, the new model describes the fractures system in more detail. However, the new model treats fractures as reservoirs with higher permeability in the central difference method, which is simpler and rougher than traditional numerical methods.

Civil aviation passengers’ comments about airlines or airports on social media are the key to improving service quality. In order to make effective use of these data, in-depth analysis is needed to provide solid support for service improvement of airlines and airports. Due to its uniqueness, accurate modeling and analysis are required. First, the data are accurately collected from various network platforms and reprocessed. In this process, transfer learning, artificial data annotation, and term frequency–inverse document frequency (TF-IDF) analysis technology are innovatively integrated to ensure data quality and analysis depth. Then, according to the characteristics of the review data, the civil aviation domain-specific word vector based on Word2Vec was customized and developed, and the backtranslation – convolutional neural networks – bi-directional long short-term memory (Backtranslation-CNN-BiLSTM) model was constructed for sentiment analysis. The model is verified by multi-dimensional evaluation indicators, which shows excellent performance indicators and ensures reasonable efficiency. Finally, the cutting-edge BERTopic modeling technology was used to deeply mine the passenger comment topics to reveal the focus and potential needs of passengers. This study successfully constructed the technical system of civil aviation passenger comment sentiment analysis, which provided technical support for industry service optimization.