Tourism destination image perception aims to depict the urban tourism image from the perspective of the perception of tourists, which, therefore, sheds new light on the advancement and innovation of urban tourism. The model proposed in this study can effectively describe the image perception of a tourism destination, with its research conclusions providing a vital referential basis for the sustainable development of urban tourism. Combined with LDA, we construct the research framework of tourism destination image perception and then take the online comments of popular scenic spots in Wuhan on Ctrip Travel as an example. The results show that four aspects are included in tourists’ perception of the city image of Wuhan: experience, history culture, leisure service, and tourist destination. Among them, the social network of the experience dimension is most closely related. In addition, emotion analysis illustrates that tourists’ emotional tendencies tend to be positive under the four perceptual dimensions.

With the growing popularity of traditional Chinese medicine (TCM) in the world and the increasing awareness of intellectual property protection, the number of TCM patent application is growing year by year. TCM patents contain rich medical, legal, and economic information. Effective text mining of TCM patents is of great theoretical and practical significance (e.g., the R&D of new medicines, patent infringement litigation, and patent acquisition). Named entity recognition (NER) is a fundamental task in natural language processing and a crucial step before indepth analysis of TCM patent. In this paper, a method combining Bidirectional Long Short‐Term Memory neural network with Conditional Random Field (BiLSTM‐CRF) is proposed to automatically recognize entities of interest (i.e., herb names, disease names, symptoms, and therapeutic effects) from the abstract texts of TCM patents. By virtue of the capabilities of deep learning methods, the semantic information in the context can be learned without feature engineering. Experiments show that the BiLSTM‐CRF‐based method provides superior performance in comparison with various baseline methods.

With the advent of the information epoch and the development of Big Data, users are constantly overwhelmed in massive information online. As an effective method to deal with this dilemma of information overload, recommendation system has become a popular area of research for the past few years. However, most of the current recommendation models fail to make the most of the ample resources hidden behind auxiliary data and user reviews. For this reason, we put forward the FHRec model. We try to combine heterogeneous information network with deep learning technology to improve recommendation performance. The working mechanism is to represent the rich auxiliary data by using heterogeneous information network, learning the features of entities through network embedding; in the meantime, by adopting deep learning technology to mine the features of entities from review texts, using the attention mechanism to fuse feature vectors of entities, inputting them into the neural network for recommendation; the last step is that we use Yelp dataset and Douban movie dataset to verify the FHRec model. The experimental results show that the FHRec behaves better than the traditional comparison algorithm on the Yelp dataset and Douban movie dataset.